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#### Flashcard 4730804112652

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#backpropagation #deep-learning #has-images #optimizer
[unknown IMAGE 4730797296908]
Question
The fundamental trick in deep learning is to use this score as a feedback signal to adjust the value of the weights a little, in a direction that will lower the loss score for the current example (see figure 1.9). This adjustment is the job of the [...], which implements what’s called the Backpropagation algorithm: the central algorithm in deep learning.
optimizer

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score as a feedback signal to adjust the value of the weights a little, in a direction that will lower the loss score for the current example (see figure 1.9). This adjustment is the job of the <span>optimizer, which implements what’s called the Backpropagation algorithm: the central algorithm in deep learning. <span>

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#### Flashcard 4731990052108

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#FEM #RES.2-002 #linear-analysis
Question
Enumerate the four steps that have to be performed in the analysis of an engineering system or of a physical system that we want to analyze.

The analysis of an engineering system requires:

• idealization of system
• formulation of equilibrium equations
• solution of equations
• interpretation of results

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The analysis of an engineering system requires: idealization of system formulation of equilibrium equations solution of equations interpretation of results

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#### Flashcard 4732010499340

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#FEM #RES.2-002 #discrete-systems #linear-analysis
Question

Steps involved in the analysis of discrete systems:

• system idealization into elements
• evaluation of element equilibrium requirements
• element assemblage
• [...]
solution of response

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Steps involved in the analysis of discrete systems: system idealization into elements evaluation of element equilibrium requirements element assemblage solution of response

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#### Flashcard 4789229718796

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#MLBook #binary-classification #has-images #logistic-regression #machine-learning #problem-statement #sigmoid-function #standard-logistic-function
Question
State the problem in logistic regression.
[unknown IMAGE 4773033413900]

In logistic regression, we still want to model $$y_i$$ as a linear function of $$\mathbf x_i$$, however, with a binary $$y_i$$ this is not straightforward. The linear combination of features such as $$\mathbf w \mathbf x_i + b$$ is a function that spans from minus infinity to plus infinity, while $$y_i$$ has only two possible values.

At the time where the absence of computers required scientists to perform manual calculations, they were eager to find a linear classification model. They figured out that if we define a negative label as 0 and the positive label as 1, we would just need to find a simple continuous function whose codomain is (0 , 1). In such a case, if the value returned by the model for input $$\mathbf x$$ is closer to 0, then we assign a negative label to $$\mathbf x$$ ; otherwise, the example is labeled as positive. One function that has such a property is the standard logistic function (also known as the sigmoid function):

$$f(x) = \displaystyle \frac{1}{1 + e^{-x}}$$,

where $$e$$ is the base of the natural logarithm (also called Euler’s number; $$e^x$$ is also known as the $$exp(x)$$ function in programming languages). Its graph is depicted in Figure 3.

The logistic regression model looks like this:
$$f_{\mathbf w, b} (\mathbf x) \stackrel{\textrm{def}}{=} \displaystyle \frac{1}{1 + e^{-(\mathbf w \mathbf x + b)}} \quad (3)$$

You can see the familiar term $$\mathbf w \mathbf x + b$$ from linear regression.

By looking at the graph of the standard logistic function, we can see how well it fits our classification purpose: if we optimize the values of $$\mathbf w$$ and $$b$$ appropriately, we could interpret the output of $$f( \mathbf x )$$ as the probability of $$y_i$$ being positive. For example, if it’s higher than or equal to the threshold 0.5 we would say that the class of $$\mathbf x$$ is positive; otherwise, it’s negative. In practice, the choice of the threshold could be different depending on the problem. We return to this discussion in Chapter 5 when we talk about model performance assessment.

Now, how do we find optimal $$\mathbf w^\ast$$ and $$b^\ast$$? In linear regression, we minimized the empirical risk which was defined as the average squared error loss, also known as the mean squared error or MSE.

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In logistic regression, we still want to model $$y_i$$ as a linear function of $$\mathbf x_i$$, however, with a binary $$y_i$$ this is not straightforward. The linear combination of features such as $$\mathbf w \mathbf x_i + b$$ is a function that spans from minus infinity to plus infinity, while $$y_i$$ has only two possible values. At the time where the absence of computers required scientists to perform manual calculations, they were eager to find a linear classification model. They figured out that if we define a negative label as 0 and the positive label as 1, we would just need to find a simple continuous function whose codomain is (0 , 1). In such a case, if the value returned by the model for input $$\mathbf x$$ is closer to 0, then we assign a negative label to $$\mathbf x$$ ; otherwise, the example is labeled as positive. One function that has such a property is the standard logistic function (also known as the sigmoid function): $$f(x) = \displaystyle \frac{1}{1 + e^{-x}}$$, where $$e$$ is the base of the natural logarithm (also called Euler’s number; $$e^x$$ is also known as the $$exp(x)$$ function in programming languages). Its graph is depicted in Figure 3. The logistic regression model looks like this: $$f_{\mathbf w, b} (x) \stackrel{\textrm{def}}{=} \displaystyle \frac{1}{1 + e^{-(\mathbf w \mathbf x + b)}} \quad (3)$$ You can see the familiar term $$\mathbf w \mathbf x + b$$ from linear regression. By looking at the graph of the standard logistic function, we can see how well it fits our classification purpose: if we optimize the values of $$\mathbf w$$ and $$b$$ appropriately, we could interpret the output of $$f( \mathbf x )$$ as the probability of $$y_i$$ being positive. For example, if it’s higher than or equal to the threshold 0.5 we would say that the class of $$\mathbf x$$ is positive; otherwise, it’s negative. In practice, the choice of the threshold could be different depending on the problem. We return to this discussion in Chapter 5 when we talk about model performance assessment. Now, how do we find optimal $$\mathbf w^\ast$$ and $$b^\ast$$? In linear regression, we minimized the empirical risk which was defined as the average squared error loss, also known as the mean squared error or MSE.

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#### Flashcard 4789254098188

Tags
#MLBook #SVM #has-images #linear-regression #machine-learning
Question
Compare the SVM and linear regression models.
[unknown IMAGE 4769622658316]

You could have noticed that the form of our linear model in eq. 1 $$\left[ f_{\mathbf w,b} (\mathbf x) = \mathbf w \mathbf x + b \right]$$ is very similar to the form of the SVM model. The only difference is the missing sign operator. The two models are indeed similar. However, the hyperplane in the SVM plays the role of the decision boundary: it’s used to separate two groups of examples from one another. As such, it has to be as far from each group as possible.

On the other hand, the hyperplane in linear regression is chosen to be as close to all training examples as possible.

You can see why this latter requirement is essential by looking at the illustration in Figure 1. It displays the regression line (in red) for one-dimensional examples (blue dots). We can use this line to predict the value of the target $$y$$ new for a new unlabeled input example $$x_{new}$$ new . If our examples are $$D$$-dimensional feature vectors (for $$D > 1$$), the only difference with the one-dimensional case is that the regression model is not a line but a plane (for two dimensions) or a hyperplane (for $$D > 2$$).

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You could have noticed that the form of our linear model in eq. 1 $$\left[ f_{\mathbf w,b} (\mathbf x) = \mathbf w \mathbf x + b \right]$$ is very similar to the form of the SVM model. The only difference is the missing sign operator. The two models are indeed similar. However, the hyperplane in the SVM plays the role of the decision boundary: it’s used to separate two groups of examples from one another. As such, it has to be as far from each group as possible. On the other hand, the hyperplane in linear regression is chosen to be as close to all training examples as possible. You can see why this latter requirement is essential by looking at the illustration in Figure 1. It displays the regression line (in red) for one-dimensional examples (blue dots). We can use this line to predict the value of the target $$y$$ new for a new unlabeled input example $$x_{new}$$ new . If our examples are $$D$$-dimensional feature vectors (for $$D > 1$$), the only difference with the one-dimensional case is that the regression model is not a line but a plane (for two dimensions) or a hyperplane (for $$D > 2$$).

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#### Flashcard 4789274545420

Tags
#MLBook #has-images #linear-regression #machine-learning #overfitting
Question
Discuss about overfitting in linear regression.
[unknown IMAGE 4789270351116]
One practical justification of the choice of the linear form for the model is that it’s simple. Why use a complex model when you can use a simple one? Another consideration is that linear models rarely overfit. Overfitting is the property of a model such that the model predicts very well labels of the examples used during training but frequently makes errors when applied to examples that weren’t seen by the learning algorithm during training. An example of overfitting in regression is shown in Figure 2. The data used to build the red regression line is the same as in Figure 1. The difference is that this time, this is the polynomial regression with a polynomial of degree 10. The regression line predicts almost perfectly the targets almost all training examples, but will likely make significant errors on new data, as you can see in Figure 1 for $$x_{new}$$ . We talk more about overfitting and how to avoid it Chapter 5.

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One practical justification of the choice of the linear form for the model is that it’s simple. Why use a complex model when you can use a simple one? Another consideration is that linear models rarely overfit. Overfitting is the property of a model such that the model predicts very well labels of the examples used during training but frequently makes errors when applied to examples that weren’t seen by the learning algorithm during training. An example of overfitting in regression is shown in Figure 2. The data used to build the red regression line is the same as in Figure 1. The difference is that this time, this is the polynomial regression with a polynomial of degree 10. The regression line predicts almost perfectly the targets almost all training examples, but will likely make significant errors on new data, as you can see in Figure 1 for $$x_{new}$$ . We talk more about overfitting and how to avoid it Chapter 5.

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#### Flashcard 4842352938252

Question

Sculley et. al., 2014, (Machine Learning):

Refactoring machine learning libraries, adding better unit tests, and associated activity is time well spent but does not necessarily [...].

address debt at a systems level

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ve a larger system-level complexity that can create hidden debt. Thus, refactoring these libraries, adding better unit tests, and associated activity is time well spent but does not necessarily <span>address debt at a systems level. <span>

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#### Flashcard 4846350896396

Tags
#knowledge-base-construction #machine-learning #unfinished
Question
In Fonduer,a relationship between n entities is represented as [...]
an n -ary relation $$R(e_1 , e_2 , . . . , e_n )$$

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A relationship between n entities is represented as an n -ary relation $$R(e_1 , e_2 , . . . , e_n )$$ and is described by a schema $$S_R (T_1 , T_2 , . . . , T_n )$$ where $$e_i \in T_i$$ .

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#### Annotation 4864509349132

 #machine-learning #software-engineering #unfinished The seminal book on Continuous Delivery was by Jez Humble and David Farley. Humble and Farley defined Continuous Delivery in their seminal book as: “... a software engineering approach in which teams produce software in short cycles, ensuring that the software can be reliably released at any time”,

Sato,Wider,Windheuser_2019_Continuous-delivery_thoughtworks,com
er risks, by allowing feedback to be incorporated into the process. What is CD4ML? To understand CD4ML, we need to first understand Continuous Delivery (CD) and where its principles originated. <span>Continuous Delivery as Jez Humble and David Farley defined it in their seminal book is: “... a software engineering approach in which teams produce software in short cycles, ensuring that the software can be reliably released at any time”, which can be achieved if you “...create a repeatable, reliable process for releasing software, automate almost everything and build quality in.” They also state: “Continuous Delivery is

#### Annotation 4864528223500

 #machine-learning #software-engineering #unfinished Because of the system-level complexity of machine-learning code, monitoring of system behavior in real time is critical.

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#### Flashcard 4871716474124

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For software including machine learning, not only do we have to manage the code artifacts but also the data sets, the [...], and the parameters and hyperparameters used by the models.
machine learning models

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Not only do we have to manage the software code artifacts but also the data sets, the machine learning models, and the parameters and hyperparameters used by such models. All these artifacts have to be managed, versioned and promoted through different stages until they’re deployed to production

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Sato,Wider,Windheuser_2019_Continuous-delivery_thoughtworks
icient collaboration and alignment. However, this integration also brings new challenges when compared to traditional software development. These include: A higher number of changing artifacts. <span>Not only do we have to manage the software code artifacts but also the data sets, the machine learning models, and the parameters and hyperparameters used by such models. All these artifacts have to be managed, versioned and promoted through different stages until they’re deployed to production. It’s harder to achieve versioning, quality control, reliability, repeatability and audibility in that process. Size and portability: Training data and machine learning models usually co

#### Flashcard 4886995799308

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#knowledge-base-construction #machine-learning
Question
Alexandria creates a probabilistic program, [...] to retrieve the facts, schemas, and entities from a text.
which it inverts

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Alexandria [12] also makes use of a probabilistic machine learning model. Alexandria creates a probabilistic program, which it inverts to retrieve the facts, schemas, and entities from a text. Alexandria does not require any supervision (only a single seed example is required)

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#### Flashcard 4896706399500

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#machine-learning #management #software-engineering #unfinished
Question
At Google in 2012, even a small team has at its disposal the power of [...], allowing the team to quickly create complex and powerful products and services.
many internal services

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Even a small team has at its disposal the power of many internal services, allowing the team to quickly create complex and powerful products and services. Design, testing, production, and maintenance pro- cesses are simplified.

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#### Flashcard 4920431480076

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#machine-learning #software-engineering #unfinished
Question
To be productionized, the model has to be adapted to the production environment. ... The productionized version of the model has to [...] before it can be deployed to production.
be tested again in conjunction with other components of the overall architecture

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uction environment. This could mean containerization of the model code or even transforming it to a high-performance language like Java or C++ ... The productionized version of the model has to <span>be tested again in conjunction with other components of the overall architecture before it can be deployed to production. <span>

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Sato,Wider,Windheuser_2019_Continuous-delivery_thoughtworks,com
test scripts — for example, for chatbots. The tests should be as automated as possible with the help of test environments, test scripts or test programs. Once a good model is found, it’s ready <span>to be productionized. The model has to be adapted to the production environment. This could mean containerization of the model code or even transforming it to a high-performance language like Java or C++ — either manually or using automatic transformation tools. The productionized version of the model has to be tested again in conjunction with other components of the overall architecture before it can be deployed to production. In production, we have to observe and monitor how the model behaves “in the wild”. Metrics like usage, model input, model output, and possible model bias are important information about

#### Annotation 4954923339020

 #machine-learning A calibration Layer is typically to account for prediction bias. A calibration layer is a post-prediction adjustment to make predictions and predicted probabilities closer to the distribution of an observed set of labels.

2019-Calibration_Layer-dataplot,co
Calibration Layer A post-prediction adjustment, typically to account for prediction bias . The adjusted predictions and probabilities should match the distribution of an observed set of labels.

#### Flashcard 4956521106700

Question
Zoonotic pathogen
A microorganism that is a colonizer or pathogen in animals and that can be transmitted to humans either via an insect vector or via direct contact with the animal or its products.

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#### Flashcard 4956522941708

Question
Environmental pathogen
A microorganism capable of causing disease that is transmitted to humans from an environmental source such as water or soil.

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#### Annotation 4960661409036

 #43 #Cours #Facultaires #Médecine #Pédiatrie #X-Fragile Si la PCR ne détecte aucun allèle de petite taille chez le garçon ou un seul allèle chez une fille, le laboratoire recherche ensuite une grande expansion et établit le profil de méthylation par southern blot

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#### Annotation 4960665079052

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Le certificat médical, qui est un document destiné à faire preuve, est différent du rapport de réquisition ou d'expertise. Ces deux types de rapports étant eux directement remis à l'autorité requérante (réquisition) ou commettante (expertise Il existe dans ces deux cas une dérogation légale et obligatoire au secret professionnel, et non au patient comme c'est le cas pour un certificat médical

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#### Annotation 4960666127628

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Rédaction obligatoire Les certificats doivent être délivrés à chaque fois que leur rédaction est prévue par un texte. Nous citerons particulièrement les certificats de : Naissance, décès, grossesse, interruption volontaire ou thérapeutique de grossesseLégislation sociale : accident du travail, maladie professionnelleSoins psychiatriques sous contrainteVaccination, suivi de la santé du patient mineur (Certificats des premières années de vie)

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#### Annotation 4960667176204

 #9 #Certificats #Cours #Facultaires #Légale #Médecine « [...] est puni d'un an d'emprisonnement et de 15 000 euros d'amende le fait d'établir une attestation ou un certificat faisant état de faits matériellement inexacts. »

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#### Annotation 4960668224780

 #9 #Certificats #Cours #Facultaires #Légale #Médecine En général, tout médecin peut rédiger un certificat. Les internes, sous réserve d'être autorisé par le chef de service, peuvent établir des certificats. Pour les certificats de décès, il faut obligatoirement que le médecin soit Docteur en Médecine, c'est-à-dire qu'il ait soutenu sa thèse En cas de remplacement d'un médecin par un interne ou un médecin non thésé, le remplaçant, autorisé par l'ordre des médecins, peut établir des certificats de décès dans le cadre uniquement de ce remplacement Pour certaines fédérations sportives « à risques », le certificat de non contre-indication à la pratique du sport en question doit être rédigé par un médecin titulaire d'un diplôme de médecine du sport, voire un médecin agréé par la fédération sportive

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#### Annotation 4960669273356

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Les constatations sont des faits objectifs recueillis lors de l'examen du sujet. Elles peuvent être aussi bien positives que négatives. Elles doivent être exhaustives, scrupuleuses et précises. Elles sont affirmées car observées par le praticien. Le médecin ne doit jamais (hors cadre d'une réquisition) interpréter l'origine des blessures constatées objectivement

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#### Annotation 4960670321932

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Habituellement, un diagnostic ne doit pas être indiqué sur un certificat médical. Celui-ci pourra en effet passer ultérieurement par de nombreuses mains non médicales. Il faut donc respecter (sauf certains cas particuliers) ce principe déontologique, en expliquant les difficultés potentielles au patient. Le risque est de nuire aux intérêts du patient et de porter atteinte à sa dignité, ce qui est contraire à la déontologie médicale « Le médecin, au service de l'individu et de la santé publique, exerce sa mission dans le respect de la vie humaine, de la personne, et de sa dignité » (article R.4127-2 du Code de la santé publique)

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#### Annotation 4960671370508

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Certains éléments sont évidents, mais parfois oubliés dans le certificat : L'identité du patient Si le sujet n'est pas connu du médecin et s'il n'a pas de pièce d'identité, il faut utiliser des formules de prudence : « déclarant se nommer » L'identité du médecinLa date exacte de l'examen (il ne faut jamais antidater ou postdater un certificat médical)La date du certificat (si elle est différente de la date de l'examen)La signature du médecin

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#### Annotation 4960672419084

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Certificats - Forme : Papier libre, ordonnance avec identification, formulaires pré-imprimés.Écriture lisible : la lisibilité est un élément important, souvent non respecté. L'illisibilité de certaines parties du certificat peut nuire au patient et engager la responsabilité du rédacteur.Style clair, simple, précis (phrases courtes).Rédaction en français

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#### Annotation 4960673467660

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Le certificat doit être « établi à la demande de l'intéressé et remis en mains propres ». Cette phrase doit être indiquée en conclusion du certificat médical

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#### Annotation 4960674516236

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Il ne faut jamais remettre un certificat à : Un avocat, à la police ou la gendarmerie En dehors des rapports de réquisition La Justice En dehors des rapports de réquisition ou d'expertise En parent ou au conjoint En effet, certains certificats remis au conjoint ont été utilisés par celui-ci dans une procédure de divorce, bien entendu ignorée du médecin : il s'agit d'une violation du secret professionnel et les médecins peuvent être condamnés dans ces circonstances

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#### Annotation 4960675564812

 #9 #Certificats #Cours #Facultaires #Légale #Médecine En matière civile, la victime doit en effet apporter la preuve de son dommage et le lien (imputabilité médicale) entre la faute et le dommage. Le certificat médical initial, qui correspond à la première constatation médicale à la suite des faits allégués, doit être rédigé avec le plus grand soin, décrivant avec exhaustivité les allégations et les constatations positives et négatives. En législation sociale (accidents de travail et maladies professionnelles), bien qu'il existe une présomption d'imputabilité (en dehors de la rechute), le certificat médical initial est également fondamental pour établir les blessures ou conséquences médicales d'un événement en rapport avec le travail

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#### Annotation 4960676613388

 #9 #Certificats #Cours #Facultaires #Légale #Médecine En matière pénale, le certificat de coups et blessures volontaires et involontaires participe au choix du tribunal compétent (qui jugera l'auteur) et au niveau de la peine encourue par l'auteur, par la fixation de l'ITT (incapacité totale de travail) au sens du Code pénal (voir ci-après). Le certificat concerne donc en premier lieu le responsable supposé des faits (l'agresseur), plutôt que la victime.

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#### Annotation 4960677661964

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Le médecin peut engager sa responsabilité pénale lorsqu'il commet une infraction au Code pénal. Il en est ainsi lorsqu'il rédige un certificat mensonger (infraction de faux et usage), en particulier quand la date de l'examen clinique du patient ne correspond pas à la réalité.Une violation du secret professionnel constitue également une infraction (un délit)

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#### Annotation 4960678710540

 #9 #Certificats #Cours #Facultaires #Légale #Médecine La responsabilité pénale est ici également présente (article L.471-4 du Code de la sécurité sociale : tout médecin ayant, dans les certificats, sciemment dénaturé les conséquences d'un accident ou de la maladie est puni d'une amende de 12 000 euros et d'un emprisonnement de trois mois)

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#### Annotation 4960679759116

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Le médecin peut engager sa responsabilité disciplinaire (déontologique) lorsqu'il rédige un certificat mensonger ou de complaisance

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#### Annotation 4960680807692

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Certificat médical Le médecin peut engager sa responsabilité civile : il est évident que si à l'occasion d'une de ces « erreurs » le médecin crée un dommage au patient, il peut engager sa responsabilité sur le plan civil et peut être condamné à lui verser une indemnisation pour compenser ce préjudice

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#### Annotation 4960713313548

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Le certificat de constat de blessures est considéré comme obligatoire car il est nécessaire au patient pour faire valoir ses droits

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#### Annotation 4960714362124

 #9 #Certificats #Cours #Facultaires #Légale #Médecine En tout état de cause, ce n'est pas au médecin de se prononcer dans le certificat sur le caractère volontaire ou involontaire des blessures (qui sera établi par l'enquête et la Justice)

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#### Annotation 4960715410700

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Soulignons le caractère plutôt précis que concis des descriptions, l'exhaustivité indispensable dans la description des blessures et l'importance parfois de mentionner un élément négatif, comme l'absence d'un symptôme. La date et l'heure de l'examen doivent être précisées

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#### Annotation 4960716459276

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Érythème : rougeur due à la vasodilatation des vaisseaux superficiels de la peau (analogue au coup de soleil). Le mécanisme est un traumatisme contondant plat, sans relief, et pas trop violent, comme une gifle

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#### Annotation 4960717507852

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Érosion superficielle : On distingue : Les érosions superficielles linéaires ou arciformes, engendrées par tout élément plus ou moins pointu qui se déplace plus ou moins parallèlement à la peau Ex : griffure, action de la pointe d'un couteau qui se déplace plus ou moins tangentiellement à la peau Les surfaces d'abrasion, appelées également dermabrasions : l'exemple typique est le frottement par le sol à l'occasion d'une chute ou d'une projection, ou encore un mécanisme de pression perpendiculaire ou très peu oblique (comme l'impression d'un tampon) Ceci se voit dans les accidents automobiles, par exemple, avec l'impression de certaines parties du véhicule sur la peau, avec une surface d'abrasion reproduisant la forme de la partie qui a été en contact

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#### Annotation 4960719342860

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Contusion : Les contusions sont par définition des traumatismes fermés, dus à des mécanismes contondants, donc un élément mousse sans aspérité, comme une matraque, le sol ou un mur lisse.Les contusions sont classées classiquement dans plusieurs catégories : Ecchymose (infiltration tissulaire de sang extravasé et coagulé, comme une goutte de colorant qui imprégnerait les mailles tissulaires d'une compresse)Hématome (collection de sang dans une cavité néoformée), écrasement et broiement

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 #9 #Certificats #Cours #Facultaires #Légale #Médecine Décrire la couleur des contusions est important. Le sang coagulé se dégrade progressivement. L'hémolyse permet la libération de l'hémoglobine et sa transformation par les cellules macrophagiques en pigments. Cette coloration se résorbe de la périphérie vers le centre.

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#### Annotation 4960721440012

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Le plus important est de décrire la couleur, car on ne demande pas au médecin non spécialiste (non légiste) de se prononcer sur la date d'une lésion : Violacée-noirâtre (un à trois jours) : lésion récentePuis verdâtre (cinq à six jours) : lésion semi-récente Puis brun-jaunâtre (dix à quinze jours) : lésions ancienne

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#### Annotation 4960723275020

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Plaie nette ou plaie simple : La plaie est une « solution de continuité », c'est-à-dire une ouverture de la peau. Dans une plaie simple ou nette, les berges sont très nettes et il n'y a pas de pont tissulaire dans la plaie.

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#### Annotation 4960724323596

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Plaie contuse : C'est une lésion extrêmement fréquente constituant également « une solution de continuité ». La peau cette fois est écrasée et se déchire. Ceci explique que les bords de la plaie soient très irréguliers (contrairement à la plaie nette) et qu'il existe des ponts tissulaires persistants dans la plaie.On note également un décollement sous-cutané des berges. Ces lésions sont dues à des traumatismes contondants suffisamment violents pour dépasser les capacités de résistance de la peau, ou au fait que la peau est écrasée contre une surface osseuse sous-jacente, comme l'arcade sourcilière, les lèvres, la pommette, le cuir chevelu

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#### Annotation 4960725372172

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Cas particulier : les blessures par projectiles d'armes à feu : les variantes possibles sont innombrables en fonction du type de l'arme et de la munition.Très schématiquement, on distingue les projectiles uniques (balles) et les projectiles multiples (plombs de chasse, chevrotines).

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#### Annotation 4960726420748

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Les médecins doivent récupérer les projectiles avec une pince en plastique à l'occasion des soins et les conserver dans un récipient sec jusqu'à leur transmission aux forces de l'ordre.Les résidus de tir doivent être soigneusement décrits avant désinfection et traitement des plaies.Idéalement, des photographies devraient être prises avant les soins

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#### Annotation 4960727469324

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Attention, il existe des erreurs assez communes : Les lésions dentaires sont souvent oubliéesLa terminologie médico-légale n'est pas toujours bien utilisée : L'erreur la plus fréquente est la confusion entre hématome et ecchymose. Pourtant, la précision de la terminologie permet de comprendre le mécanisme qui a occasionné la lésion Il faut éviter les qualificatifs inutiles ou spectaculaires (« situation dramatique », « hémorragie considérable », « grande violence »…), qui n'apportent rien de plus, sauf de la subjectivité (or il vous est demandé d'être objectif)Ne pas écrire « ce certificat ne peut pas être produit en justice » : car il peut toujours l'êtreNe pas écrire « pour faire valoir ce que de droit » à la fin du certificat, cela n'apporte rien de plus.

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#### Annotation 4962446347532

 The circlejerk on r/programming would have you believe agile is terrible (see Google's search results for agile in /r/programming). We see bombastic headlines excoriating agile:

rk to learn the rest of the keyboard shortcuts Jump to content log insign up User account menu 4 Alternatives to Agile? Close 4 Posted by u/matthedev 4 years ago Archived Alternatives to Agile? <span>The circlejerk on r/programming would have you believe agile is terrible (see Google's search results for agile in /r/programming). We see bombastic headlines excoriating agile: The Failure of Agile Why “Agile” and especially Scrum are terrible AGILE must be destroyed, once and for all - Erik Meijer Clearly a lot of developers have a visceral loathing of agile. Myself, I've only worked in environments where we used waterfall (Rational Unified Process, to be more precise) and agile

#### Annotation 4962448444684

 Myself, I've only worked in environments where we used waterfall (Rational Unified Process, to be more precise) and agile (Scrum, XP). After spending years working in a waterfall environment, agile was a breath of fresh air. I have come to associate waterfall with things that can make a developer hate their job: rigid process, heavy top-down control, lack of creative autonomy, stale tools, little to no or heavily delayed feedback, heavy technical debt, and apathetic colleagues. Agile, to me, represents the opposite.

agile: The Failure of Agile Why “Agile” and especially Scrum are terrible AGILE must be destroyed, once and for all - Erik Meijer Clearly a lot of developers have a visceral loathing of agile. <span>Myself, I've only worked in environments where we used waterfall (Rational Unified Process, to be more precise) and agile (Scrum, XP). After spending years working in a waterfall environment, agile was a breath of fresh air. I have come to associate waterfall with things that can make a developer hate their job: rigid process, heavy top-down control, lack of creative autonomy, stale tools, little to no or heavily delayed feedback, heavy technical debt, and apathetic colleagues. Agile, to me, represents the opposite. If people think agile is terrible, what alternatives are people advocating or putting to use? 2 comments share save hide report 100% Upvoted This thread is archived New comments cannot

#### Annotation 4962450017548

 If people think agile is terrible, what alternatives are people advocating or putting to use?

heavy top-down control, lack of creative autonomy, stale tools, little to no or heavily delayed feedback, heavy technical debt, and apathetic colleagues. Agile, to me, represents the opposite. <span>If people think agile is terrible, what alternatives are people advocating or putting to use? 2 comments share save hide report 100% Upvoted This thread is archived New comments cannot be posted and votes cannot be cast Sort by best level 1 noratat 7 points · 4 years ago The pro

#### Annotation 4962452901132

 What's hilarious to me is that since the Agile manifesto is so vague, you could say that in many smally shops, its "core principles" will organically happen anyway

The Failure of Agile : programming
lmost anything can be considered Agile. Yet most "agile experts" still manage to violate the core principles. Continue this thread level 2 Tech_Itch 44 points · 4 years ago · edited 4 years ago <span>What's hilarious to me is that since the Agile manifesto is so vague, you could say that its "core principles" will organically happen in many small shops anyway: Individuals and interactions over Processes and tools: Everyone will insist on using their own tools, and fiercely defend their choice. Much time will be spent in "individual interacti

#### Annotation 4962456309004

 Behind the Agile manifesto: What the authors came to value after developing software by doing it and helping others do it. Manifesto for Agile Software Development: Individuals and interactions over processes and tools Manifesto for Agile Software Development: Working software over comprehensive documentation Manifesto for Agile Software Development: Customer collaboration over contract negotiation Manifesto for Agile Software Development: Responding to change over following a plan Manifesto for Agile Software Development: X "over" Y means that while there is value in X, Agile authors value Y more.

Unknown title
Manifesto for Agile Software Development Manifesto for Agile Software Development We are uncovering better ways of developing software by doing it and helping others do it. Through this work we have come to value: Individuals and interactions over processes and tools Working software over comprehensive documentation Customer collaboration over contract negotiation Responding to change over following a plan That is, while there is value in the items on the right, we value the items on the left more. Kent Beck Mike Beedle Arie van Bennekum Alistair Cockburn Ward Cunningham Martin Fowler James Grenning Jim Highsmith Andrew Hunt Ron Jeffries Jon Kern Brian Marick Robert C. Martin Stev

#### Annotation 4962458406156

 Trying to make a generalized set of rules for development is really hard.No, making a generalised set of rules for development is easy: "Learn from your mistakes", "Don't introduce new bugs when fixing an existing one", "Make sure you don't solve the wrong problem", "Listen to the needs of the users", "Always do the more important thing first". What's hard, of course, is to make a generalised set of rules that is actually useful.

The Failure of Agile : programming
s demanding constant changes, you will be responding to change by constantly fighting fires caused by those previous steps. AGILE! Continue this thread level 2 dimnakorr 30 points · 4 years ago <span>Trying to make a generalized set of rules for development is really hard. No, making a generalised set of rules for development is easy: "Learn from your mistakes", "Don't introduce new bugs when fixing an existing one", "Make sure you don't solve the wrong problem", "Listen to the needs of the users", "Always do the more important thing first". What's hard, of course, is to make a generalised set of rules that is actually useful. Continue this thread level 2 locster 26 points · 4 years ago The best predictor of project success is the quality of the programmers. Where Agile and its ilk fit in is in the management

#### Annotation 4962461289740

 The best coders are more or less self managingRead "the most expensive". You want cheap, easily-replaced lowest common denominator cogs. But to manage those, you need your micromanaging methodology du jour.

The Failure of Agile : programming
ew comments cannot be posted and votes cannot be cast Sort by best View all commentsShow parent comments View discussions in 4 other communities level 1 ApatheticGodzilla 8 points · 4 years ago <span>The best coders are more or less self managing Read "the most expensive". You want cheap, easily-replaced lowest common denominator cogs. But to manage those, you need your micromanaging methodology du jour. level 2 Comment deleted by user4 years ago Continue this thread level 2 [deleted] 1 point · 4 years ago That's why I quit my last job. I was tired of the micromanagement that agile is.

#### Annotation 4962464697612

 4 years ago "Agile" may work that way, but "agile" works really well with experienced developers. level 2 tequila13 7 points · 4 years ago Everything works well with experienced developers. Like, if you're Emma Watson every dress looks good on you. She could wear a trash bag and from the next day it would be the next fashion direction.

The Failure of Agile : programming
thread is archived New comments cannot be posted and votes cannot be cast Sort by best View all commentsShow parent comments View discussions in 4 other communities level 1 Tiquortoo 2 points · <span>4 years ago "Agile" may work that way, but "agile" works really well with experienced developers. level 2 tequila13 7 points · 4 years ago Everything works well with experienced developers. Like, if you're Emma Watson every dress looks good on you. She could wear a trash bag and from the next day it would be the next fashion direction. Continue this thread level 2 skulgnome 2 points · 4 years ago Anything works with people clever enough to outwit its stupid. level 2 Comment deleted by user4 years ago Continue this thr

#### Annotation 4962468105484

 Everything works well with experienced developers. Like, if you're Emma Watson every dress looks good on you. She could wear a trash bag and from the next day it would be the next fashion direction. level 2 Tiquortoo 2 points · 4 years ago Not everything works well with experienced devs. I've been around the block a bit and that is patently false.

The Failure of Agile : programming
chived New comments cannot be posted and votes cannot be cast Sort by best View all commentsShow parent comments View discussions in 4 other communities level 1 tequila13 7 points · 4 years ago <span>Everything works well with experienced developers. Like, if you're Emma Watson every dress looks good on you. She could wear a trash bag and from the next day it would be the next fashion direction. level 2 Tiquortoo 2 points · 4 years ago Not everything works well with experienced devs. I've been around the block a bit and that is patently false. level 3 tequila13 -1 points · 4 years ago I have a feeling you had issues with specific devs, not with methodologies applied by them. level 4 Tiquortoo 1 point · 4 years ago Which is wh

#### Annotation 4962471513356

 All software development mythologies are flawed and imperfect. There is no silver bullet. It is low hanging fruit to bash any methodology for its flaws. However no method will take a team of shitty developers and allow it to consistently produce successful results.

Why “Agile” and especially Scrum are terrible : programming
e project. level 2 ErstwhileRockstar -2 points · 4 years ago Guess I should just quit. Any day. Better economic prospects give you better opportunities. level 1 ggtsu_00 74 points · 4 years ago <span>All software development mythologies are flawed and imperfect. There is no silver bullet. It is low hanging fruit to bash any methodology for its flaws. However no method will take a team of shitty developers and allow it to consistently produce successful results. Conversely, any team of well rounded and talented engineers will produce consistent and good quality software no matter what method you throw at them. If agile or scrum isn't working fo

#### Annotation 4962473610508

 Agreed, with one minor edit. I think some groups and projects work better with some methodologies. Thus why people keep making new ones and jumping on their bandwagons.Over-planning can kill excitement and creativity in already-enthusiastic teams. Open-ended management can lead to confusion and disorder among teams that need more direction.

Why “Agile” and especially Scrum are terrible : programming
crappy team is usually also a sign of crappy management -- it doesn't take much to get all the good people to go somewhere else. Continue this thread level 2 luxliquidus 12 points · 4 years ago <span>Agreed, with one minor edit. I think some groups and projects work better with some methodologies. Thus why people keep making new ones and jumping on their bandwagons. Over-planning can kill excitement and creativity in already-enthusiastic teams. Open-ended management can lead to confusion and disorder among teams that need more direction. level 2 yogthos 4 points · 4 years ago I completely agree that at the end of the day it's about people. If you get a group of competent people who work well together then you'll get goo

#### Annotation 4962475183372

 I completely agree that at the end of the day it's about people. If you get a group of competent people who work well together then you'll get good results. The problem happens when you start imposing methodologies on these people.Unfortunately, in many organizations the people responsible for doing the work aren't the ones deciding how to go about accomplishing it. Management loves things like scrum because it provides an illusion of progress.

Why “Agile” and especially Scrum are terrible : programming
ll excitement and creativity in already-enthusiastic teams. Open-ended management can lead to confusion and disorder among teams that need more direction. level 2 yogthos 4 points · 4 years ago <span>I completely agree that at the end of the day it's about people. If you get a group of competent people who work well together then you'll get good results. The problem happens when you start imposing methodologies on these people. Unfortunately, in many organizations the people responsible for doing the work aren't the ones deciding how to go about accomplishing it. Management loves things like scrum because it provides an illusion of progress. level 2 [deleted] 7 points · 4 years ago If agile or scrum isn't working for you, likely no method will, and you are probably just stuck in a shitty work environment laced with poor tal

#### Annotation 4962478066956

 We learn to stop trendhopping as a proxy for knowledge? Roll back the tendency in our industry of teaching solutions before making sure the problems are well understood? Create an environment where analyzing process is a first class topic of conversation, not that thing that happens 20 minutes into the "X isn't the wrong strategy, your implementation doesn't adhere to the dogma well enough" argument (AKA the Agile Scotsman)?

AGILE must be destroyed, once and for all - Erik Meijer : programming
lent!!! Thank you internet stranger!!!! Continue this thread level 2 [deleted] 101 points · 4 years ago · edited 4 years ago the same flaws come forward in a subtly different manner, what then? <span>We learn to stop trendhopping as a proxy for knowledge? Roll back the tendency in our industry of teaching solutions before making sure the problems are well understood? Create an environment where analyzing process is a first class topic of conversation, not that thing that happens 20 minutes into the "X isn't the wrong strategy, your implementation doesn't adhere to the dogma well enough" argument (AKA the Agile Scotsman)? Ah, who am I kidding? We're in a fast moving industry, learn about and discuss advancements in pseudonymous forums online where prevailing opinions often lack any form of rigor. Forget

#### Annotation 4962480426252

 We're in a fast moving industry, learn about and discuss advancements in pseudonymous forums online where prevailing opinions often lack any form of rigor. Forget scientific rigor, does "the tests ARE the documentation" even pass the laugh test anymore? We're prime targets for hucksters. Depending on topic, the state of the art can be decades ahead of the science. Especially on topics that aren't formally provable or empirically researchable, we have people loudly banging on

AGILE must be destroyed, once and for all - Erik Meijer : programming
ot that thing that happens 20 minutes into the "X isn't the wrong strategy, your implementation doesn't adhere to the dogma well enough" argument (AKA the Agile Scotsman)? Ah, who am I kidding? <span>We're in a fast moving industry, learn about and discuss advancements in pseudonymous forums online where prevailing opinions often lack any form of rigor. Forget scientific rigor, does "the tests ARE the documentation" even pass the laugh test anymore? We're prime targets for hucksters. Depending on topic, the state of the art can be decades ahead of the science. Especially on topics that aren't formally provable or empirically researchable, we have people loudly banging on about how we're "logical" thinkers because we deal in computational logic all day. Then they get swept up by the first convincing argument they can't immediately refute, as if that's so

#### Annotation 4962481999116

 Then we recognize that building software is a craft and we stop coming up with systems that are only designed to give management that warm fuzzy feeling that is provided by the illusion they have some sort of control of the software development process.

AGILE must be destroyed, once and for all - Erik Meijer : programming
ll ever be perfect. The only thing we can do is what ever works at the end of the day and make incremental improvements over time. Continue this thread level 2 BorgDrone 45 points · 4 years ago <span>Then we recognize that building software is a craft and we stop coming up with systems that are only designed to give management that warm fuzzy feeling that is provided by the illusion they have some sort of control of the software development process. The only viable system is "leave me the fuck alone, I'll let you know when it's done". Continue this thread level 2 stormcrowsx 3 points · 4 years ago It could be worse, it could be six

#### Annotation 4962484882700

 This. Agile was supposed to just be an observation of what worked with naturally good teams. It turns out it's very hard to imitate naturally good teams and get anything like the same results.

AGILE must be destroyed, once and for all - Erik Meijer : programming
lt. If you have that core of solid people AGILE tends to happen anyway, just not with the formal standup meetings and the rest of what AGILE preaches. level 2 Procrastes 16 points · 4 years ago <span>This. Agile was supposed to just be an observation of what worked with naturally good teams. It turns out it's very hard to imitate naturally good teams and get anything like the same results. Continue this thread level 2 jayanmn Original Poster1 point · 4 years ago · edited 4 years ago Precisely! In our work we have adopted many good parts unit tests, continuous integration,

#### Annotation 4962488290572

 I think those of us who spend a lot of time in the on-line bubble/echo chamber have an unfortunate but perhaps natural tendency to follow the herd and succumb to groupthink. This is how the hucksters exploit the community.

AGILE must be destroyed, once and for all - Erik Meijer : programming
pend an hour on Hacker News and find a bunch of low-quality charlatans. edit: Oaden, you make a good point, sorry for using it for going on a tangent. level 2 Silhouette 21 points · 4 years ago <span>I think those of us who spend a lot of time in the on-line bubble/echo chamber have an unfortunate but perhaps natural tendency to follow the herd and succumb to groupthink. This is how the hucksters exploit the community. The thing is, all these people promoting specific ways of making software can do is propose ideas. You don't have to actually do what they say. It's not illegal to ignore them, and no d

#### Annotation 4962490387724

 It turns out that if you completely ignore the hype and don't write a tutorial blog post and don't debate ad nauseam in on-line forums and don't go to all the cool conferences, and instead you just carry on making software that works using boring, tried and tested tools and techniques, things will turn out just fine.

AGILE must be destroyed, once and for all - Erik Meijer : programming
g to strike you down with lightning because you disagreed. So if you want out, step off the treadmill. I've been making software for many years, and I've seen plenty of hype cycles come and go. <span>It turns out that if you completely ignore the hype and don't write a tutorial blog post and don't debate ad nauseam in on-line forums and don't go to all the cool conferences, and instead you just carry on making software that works using boring, tried and tested tools and techniques, things will turn out just fine. If you keep a critical eye on industry developments and experiment with ideas that might actually be useful to you, that's great too. Sometimes new ideas come along that really are help

#### Annotation 4962491960588

 If you keep a critical eye on industry developments and experiment with ideas that might actually be useful to you, that's great. Sometimes new ideas come along from industry developments that really are helpful. But, in software engineeing, if you go looking for a pot of gold every time someone says they saw a rainbow, you'll soon lose sight of what actually matters: making useful and/or entertaining stuff.

AGILE must be destroyed, once and for all - Erik Meijer : programming
ne forums and don't go to all the cool conferences, and instead you just carry on making software that works using boring, tried and tested tools and techniques, things will turn out just fine. <span>If you keep a critical eye on industry developments and experiment with ideas that might actually be useful to you, that's great too. Sometimes new ideas come along that really are helpful and you do better if you adopt them. But if you go looking for a pot of gold every time someone says they saw a rainbow in this business, you'll soon lose sight of what actually matters: making useful and/or entertaining stuff. Continue this thread level 2 DevIceMan 2 points · 4 years ago I've worked in businesses and industries where the process is designed around the project/problem/business. In fact, that's

#### Annotation 4962493533452

 I've worked in businesses and industries where the process is designed around the project/problem/business. In fact, that's pretty much the norm in many industries.Ultimately Agile isn't agile because it's only good for a very particular type of problem, where complexity is low, and the goal is iterative refinement, and deadlines/budgets aren't fixed. Even then, Agile might not be the best practice.

AGILE must be destroyed, once and for all - Erik Meijer : programming
says they saw a rainbow in this business, you'll soon lose sight of what actually matters: making useful and/or entertaining stuff. Continue this thread level 2 DevIceMan 2 points · 4 years ago <span>I've worked in businesses and industries where the process is designed around the project/problem/business. In fact, that's pretty much the norm in many industries. Ultimately Agile isn't agile because it's only good for a very particular type of problem, where complexity is low, and the goal is iterative refinement, and deadlines/budgets aren't fixed. Even then, Agile might not be the best practice. Before I started my Software-Dev career, and while applying places, I thought Agile was some kind of professional way programmers worked together, perhaps resolving merge conflicts, or

#### Annotation 4962496417036

 Most corporate CRUD apps are simple blunt instruments intended to help automate paper or otherwise inefficient processes. That's it. The problem is that 1) there's often no connection between the "product owner" and the user community, so adoption fails, and 2) people don't make rational decisions.

AGILE must be destroyed, once and for all - Erik Meijer : programming
e fuck alone, I'll let you know when it's done". Business: "And you wonder why you get poor reviews." Continue this thread level 2 [deleted] 10 points · 4 years ago But it's not always a craft. <span>Most corporate CRUD apps are simple blunt instruments intended to help automate paper or otherwise inefficient processes. That's it. The problem is that 1) there's often no connection between the "product owner" and the user community, so adoption fails, and 2) people don't make rational decisions. Continue this thread level 2 cc81 11 points · 4 years ago I've worked with colleagues like that. While there is that one super talented guy who can work like that most seem to be people

#### Annotation 4962498514188

 In most cases you are not a special snow flake; you are one piece in a bigger system and part of your job is to communicate. Just deal with it.Look at for example hardware development and see how much they need to sync to deliver a new product.

AGILE must be destroyed, once and for all - Erik Meijer : programming
b is just about coding. So they work on the wrong thing, they don't tell anyone when it takes twice as long to build as first estimated, they fail to communicate properly with the customer etc. <span>In most cases you are not a special snow flake; you are one piece in a bigger system and part of your job is to communicate. Just deal with it. Look at for example hardware development and see how much they need to sync to deliver a new product. Continue this thread level 2 Dark_Crystal 4 points · 4 years ago We also need to stop pushing the whole DevOps idea. Just getting things done without a lot of process overhead is great

#### Annotation 4962501397772

 Building a good team isn't easy, but if you manage to do that it's like magic. That is what managers in software companies should be doing. Instead of trying to squeeze every person into the straightjacket of their process, they should be looking for ways to optimally use a person's talents, put the right people together and let them find their own process.

AGILE must be destroyed, once and for all - Erik Meijer : programming
s a small group of people who know each others strengths and weaknesses. No one is good at everything, but a team like that will manage itself and assign tasks to whoever is best suited for it. <span>Building a good team isn't easy, but if you manage to do that it's like magic. That is what managers in software companies should be doing. Instead of trying to squeeze every person into the straightjacket of their process, they should be looking for ways to optimally use a person's talents, put the right people together and let them find their own process. And of course you should communicate, but keep it relevant. What is the point if giving estimates you pulled out of your arse just because your manager keeps insisting on it ? I underst

#### Annotation 4962504805644

 A few business rules can make developing corporate CRUD apps start feeling like a craft. Sometimes business rules are only in the minds of senior business users and not documented formally.

AGILE must be destroyed, once and for all - Erik Meijer : programming
at 1) there's often no connection between the "product owner" and the user community, so adoption fails, and 2) people don't make rational decisions. level 2 _georgesim_ 12 points · 4 years ago <span>Throw a few business rules in there and then it starts feeling like a craft. Bonus points if the business rules are only in the minds of senior business users and not documented formally. Continue this thread level 2 JBlitzen 1 point · 4 years ago Anyone who's ever used business software knows that the difference between great business software and shitty business softwa

#### Flashcard 4962522631436

Question
Training data and machine learning models usually come in [...] than the size of the software code.
come in volumes that are orders of magnitude higher

status measured difficulty not learned 37% [default] 0

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Training data and machine learning models usually come in volumes that are orders of magnitude higher than the size of the software code.

#### Original toplevel document

Sato,Wider,Windheuser_2019_Continuous-delivery_thoughtworks
rough different stages until they’re deployed to production. It’s harder to achieve versioning, quality control, reliability, repeatability and audibility in that process. Size and portability: <span>Training data and machine learning models usually come in volumes that are orders of magnitude higher than the size of the software code. As such they require different tools that are able to handle them efficiently. These tools impede the use of a single unified format to share those artifacts along the path to production, which can lead to a “throw over the wall” attitude between different teams. Different skills and working processes in the workforce: To develop machine learning applications, experts with complementary skills are necessary, and they sometimes have contradicting

#### Annotation 4962524204300

 #machine-learning #software-engineering #unfinished What#is#machin e#lear nin g? ! An #engineerin g#d iscip li ne? Yes:#applications#abound.

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What#is#machin e#lear nin g? ! An #engineerin g#d iscip li ne? Yes:#applications#abound. ! An #exact#scien ce? Yes:#statistical/algorithmic#learning#theory. ! An #experimental#science? Yes:#papers#often#report#on#experiments.

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#### Annotation 4962525777164

 #machine-learning #software-engineering #unfinished What is machine learning? An #exact#scien ce? Yes:#statistical/algorithmic#learning#theory.

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What#is#machin e#lear nin g? ! An #engineerin g#d iscip li ne? Yes:#applications#abound. ! An #exact#scien ce? Yes:#statistical/algorithmic#learning#theory. ! An #experimental#science? Yes:#papers#often#report#on#experiments.

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#### Annotation 4962528136460

 #machine-learning #software-engineering #unfinished What is machine learning? An #experimental#science? Yes:#papers#often#report#on#experiments.

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What#is#machin e#lear nin g? ! An #engineerin g#d iscip li ne? Yes:#applications#abound. ! An #exact#scien ce? Yes:#statistical/algorithmic#learning#theory. ! An #experimental#science? Yes:#papers#often#report#on#experiments.

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#### Annotation 4962746240268

 BERT can be used for a wide variety of tasks. The two pre-training objectives allow it to be used on any single sequence and sequence-pair tasks without substantial task-specific architecture modifications.

BERT Explained – A list of Frequently Asked Questions – Let the Machines Learn
ny downstream tasks such as Question and Answering and Natural Language Inference require an understanding of the relationship between two sentences. What downstream tasks can BERT be used for? <span>BERT can be used for a wide variety of tasks. The two pre-training objectives allow it to be used on any single sequence and sequence-pair tasks without substantial task-specific architecture modifications. How is the input text represented before feeding to BERT? The input representation used by BERT is able to represent a single text sentence as well as a pair of sentences (eg., [Questio

#### Annotation 4962967489804

 #bert The input representation used by BERT is able to represent a single text sentence as well as a pair of sentences (eg., [Question, Answer]) in a single sequence of tokens. The first token of every input sequence in BERT is the special classification token – [CLS].BERT: The [CLS] token is used in classification tasks as an aggregate of the entire sequence representation.BERT: The [CLS] token is ignored in non-classification tasks.BERT: For single text sentence tasks, the [CLS] token is followed by the WordPiece tokens and the separator token – [SEP].BERT: For sentence pair tasks, the WordPiece tokens of the two sentences are separated by a [SEP] token. This iFor both single and pair sentence inputs in BERT, the sequence ends with the [SEP] token. Sentence Pair Input BERT: For sentence pair input, a token's sentence embedding indicates Sentence A or Sentence B.BERT: Sentence embeddings are similar to token/word embeddings with a vocabulary of 2.BERT: positional embeddings indicate each token's position in the sequence.

BERT Explained – A list of Frequently Asked Questions – Let the Machines Learn
ctives allow it to be used on any single sequence and sequence-pair tasks without substantial task-specific architecture modifications. How is the input text represented before feeding to BERT? <span>The input representation used by BERT is able to represent a single text sentence as well as a pair of sentences (eg., [Question, Answer]) in a single sequence of tokens. The first token of every input sequence is the special classification token – [CLS]. This token is used in classification tasks as an aggregate of the entire sequence representation. It is ignored in non-classification tasks. For single text sentence tasks, this [CLS] token is followed by the WordPiece tokens and the separator token – [SEP]. Single Sentence Input For sentence pair tasks, the WordPiece tokens of the two sentences are separated by another [SEP] token. This input sequence also ends with the [SEP] token. Sentence Pair Input A sentence embedding indicating Sentence A or Sentence B is added to each token. Sentence embeddings are similar to token/word embeddings with a vocabulary of 2. A positional embedding is also added to each token to indicate its position in the sequence. Which Tokenization strategy is used by BERT? BERT uses WordPiece tokenization. The vocabulary is initialized with all the individual characters in the language, and then the most freque

#### Annotation 4963308801292

 These biases against AAVE become especially worrisome as more platforms use tools like Perspective to moderate online discussions.

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These biases against AAVE become especially worrisome as more platforms use tools like Perspective to moderate online discussions. Perspective has already partnered with Wikipedia, The New York Times, The Economist, and The Guardian. Meanwhile, social media platforms like Facebook have their own automated tools for

#### Original toplevel document

How Automated Tools Discriminate Against Black Language – MIT Center for Civic Media
ers at the University of Massachusetts have shown that several popular tools for natural language processing (NLP) tend to perform more poorly on AAVE and even misidentify AAVE as non-English . <span>These biases against AAVE become especially worrisome as more platforms use tools like Perspective to moderate online discussions. Perspective has already partnered with Wikipedia, The New York Times, The Economist, and The Guardian. Meanwhile, social media platforms like Facebook have their own automated tools for content moderation — and an unfortunate track record of disabling the accounts of Black activists while doing little about the accounts of white supremacists. There are well-documented problems of content moderation on social media platforms , but as we work to address these problems, I argue that we have to recognize that platforms can have

#### Annotation 4963310374156

 Perspective has already partnered with Wikipedia, The New York Times, The Economist, and The Guardian.

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These biases against AAVE become especially worrisome as more platforms use tools like Perspective to moderate online discussions. Perspective has already partnered with Wikipedia, The New York Times, The Economist, and The Guardian. Meanwhile, social media platforms like Facebook have their own automated tools for content moderation — and an unfortunate track record of disabling the accounts of Black activists whil

#### Original toplevel document

How Automated Tools Discriminate Against Black Language – MIT Center for Civic Media
ers at the University of Massachusetts have shown that several popular tools for natural language processing (NLP) tend to perform more poorly on AAVE and even misidentify AAVE as non-English . <span>These biases against AAVE become especially worrisome as more platforms use tools like Perspective to moderate online discussions. Perspective has already partnered with Wikipedia, The New York Times, The Economist, and The Guardian. Meanwhile, social media platforms like Facebook have their own automated tools for content moderation — and an unfortunate track record of disabling the accounts of Black activists while doing little about the accounts of white supremacists. There are well-documented problems of content moderation on social media platforms , but as we work to address these problems, I argue that we have to recognize that platforms can have

#### Annotation 4963311947020

 social media platforms like Facebook have their own automated tools for content moderation — and an unfortunate track record of disabling the accounts of Black activists while doing little about the accounts of white supremacists.

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me as more platforms use tools like Perspective to moderate online discussions. Perspective has already partnered with Wikipedia, The New York Times, The Economist, and The Guardian. Meanwhile, <span>social media platforms like Facebook have their own automated tools for content moderation — and an unfortunate track record of disabling the accounts of Black activists while doing little about the accounts of white supremacists. <span>

#### Original toplevel document

How Automated Tools Discriminate Against Black Language – MIT Center for Civic Media
ers at the University of Massachusetts have shown that several popular tools for natural language processing (NLP) tend to perform more poorly on AAVE and even misidentify AAVE as non-English . <span>These biases against AAVE become especially worrisome as more platforms use tools like Perspective to moderate online discussions. Perspective has already partnered with Wikipedia, The New York Times, The Economist, and The Guardian. Meanwhile, social media platforms like Facebook have their own automated tools for content moderation — and an unfortunate track record of disabling the accounts of Black activists while doing little about the accounts of white supremacists. There are well-documented problems of content moderation on social media platforms , but as we work to address these problems, I argue that we have to recognize that platforms can have

#### Annotation 4963313519884

 #machine-learning #software-engineering #unfinished An automated feature management tool has regularly allowed a team at Google to safely delete thousands of lines of feature-related code per quarter, and has made verification of versions and other issues automatic.

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be annotated. Automated checks can then be run to ensure that all depen- dencies have the appropriate annotations, and dependency trees can be fully resolved. Since its adoption, this approach <span>has regularly allowed a team at Google to safely delete thousands of lines of feature-related code per quarter, and has made verification of versions and other issues automatic. <span>

#### Original toplevel document (pdf)

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#### Annotation 4963315879180

 #machine-learning #software-engineering #unfinished automated feature management tool was described in [6], which enables data sources and features to be annotated. Automated checks can then be run to ensure that all depen- dencies have the appropriate annotations, and dependency trees can be fully resolved.

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A remarkably useful automated feature management tool was described in [6], which enables data sources and features to be annotated. Automated checks can then be run to ensure that all depen- dencies have the appropriate annotations, and dependency trees can be fully resolved. Since its adoption, this approach has regularly allowed a team at Google to safely delete thousands of lines of feature-related code per quarter, and has made verification of versions a

#### Original toplevel document (pdf)

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#### Annotation 4963321646348

 #machine-learning A calibration Layer is typically to account for prediction bias.

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A calibration Layer is typically to account for prediction bias . A calibration layer is a post-prediction adjustment to make predictions and predicted probabilities closer to the distribution of an observed set of labels.

#### Original toplevel document

2019-Calibration_Layer-dataplot,co
Calibration Layer A post-prediction adjustment, typically to account for prediction bias . The adjusted predictions and probabilities should match the distribution of an observed set of labels.

#### Annotation 4963323219212

 #machine-learning A calibration layer is a post-prediction adjustment to make predictions and predicted probabilities closer to the distribution of an observed set of labels.

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A calibration Layer is typically to account for prediction bias . A calibration layer is a post-prediction adjustment to make predictions and predicted probabilities closer to the distribution of an observed set of labels.

#### Original toplevel document

2019-Calibration_Layer-dataplot,co
Calibration Layer A post-prediction adjustment, typically to account for prediction bias . The adjusted predictions and probabilities should match the distribution of an observed set of labels.

#### Annotation 4963325054220

 #knowledge-base-construction #machine-learning #nlp #unfinished built-in types are the main way that prior knowledge is made available to the model.

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The core idea is that entity types are compound types learned from data, constructed from built-in primitive types whose parameters are also learned from data. These built-in types are the main way that prior knowledge is made available to the model.

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#### Annotation 4963326627084

 #knowledge-base-construction #machine-learning #nlp #unfinished ntity types are compound types learned from data, constructed from built-in primitive types whose parameters are also learned from data.

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The core idea is that entity types are compound types learned from data, constructed from built-in primitive types whose parameters are also learned from data. These built-in types are the main way that prior knowledge is made available to the model.

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#### Annotation 4963399765260

 #machine-learning #software-engineering #unfinished code dependencies can be relatively easy to identify via static analysis, linkage graphs, and the like

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Furthermore, while code dependencies can be relatively easy to identify via static analysis, linkage graphs, and the like, it is far less common that data dependencies have similar analysis tools. Thus, it can be inappropriately easy to build large data-dependency chains

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#### Annotation 4963401338124

 #machine-learning #software-engineering #unfinished As it is uncommon to have analysis tools available for data dependencies, it can be inappropriately easy to build large data-dependency chains

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Furthermore, while code dependencies can be relatively easy to identify via static analysis, linkage graphs, and the like, it is far less common that data dependencies have similar analysis tools. Thus, it can be inappropriately easy to build large data-dependency chains

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#### Annotation 4963549711628

 #machine-learning #software-engineering #unfinished The seminal book on Continuous Delivery was by Jez Humble and David Farley.

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The seminal book on Continuous Delivery was by Jez Humble and David Farley. Humble and Farley defined Continuous Delivery in their seminal book as: “... a software engineering approach in which teams produce software in short cycles, ensuring that the software

#### Original toplevel document

Sato,Wider,Windheuser_2019_Continuous-delivery_thoughtworks,com
er risks, by allowing feedback to be incorporated into the process. What is CD4ML? To understand CD4ML, we need to first understand Continuous Delivery (CD) and where its principles originated. <span>Continuous Delivery as Jez Humble and David Farley defined it in their seminal book is: “... a software engineering approach in which teams produce software in short cycles, ensuring that the software can be reliably released at any time”, which can be achieved if you “...create a repeatable, reliable process for releasing software, automate almost everything and build quality in.” They also state: “Continuous Delivery is

#### Annotation 4963551284492

 #machine-learning #software-engineering #unfinished Humble and Farley defined Continuous Delivery in their seminal book as: “... a software engineering approach in which teams produce software in short cycles, ensuring that the software can be reliably released at any time”,

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The seminal book on Continuous Delivery was by Jez Humble and David Farley. Humble and Farley defined Continuous Delivery in their seminal book as: “... a software engineering approach in which teams produce software in short cycles, ensuring that the software can be reliably released at any time”,

#### Original toplevel document

Sato,Wider,Windheuser_2019_Continuous-delivery_thoughtworks,com
er risks, by allowing feedback to be incorporated into the process. What is CD4ML? To understand CD4ML, we need to first understand Continuous Delivery (CD) and where its principles originated. <span>Continuous Delivery as Jez Humble and David Farley defined it in their seminal book is: “... a software engineering approach in which teams produce software in short cycles, ensuring that the software can be reliably released at any time”, which can be achieved if you “...create a repeatable, reliable process for releasing software, automate almost everything and build quality in.” They also state: “Continuous Delivery is

#### Annotation 4963614199052

 #machine-learning #nlp #unfinished where the gap between the relevant information and the place that it’s needed is small, RNNs can learn to use the past information.

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previous ones. If we are trying to predict the last word in “the clouds are in the sky,” we don’t need any further context – it’s pretty obvious the next word is going to be sky. In such cases, <span>where the gap between the relevant information and the place that it’s needed is small, RNNs can learn to use the past information. But there are also cases where we need more context. Consider trying to predict the last word in the text “I grew up in France… I speak fluent French.” Recent information suggests that

#### Original toplevel document

Olah-2015-Understanding_LSTM_Networks-colah,github,io
derstanding of the present frame. If RNNs could do this, they’d be extremely useful. But can they? It depends. Sometimes, we only need to look at recent information to perform the present task. <span>For example, consider a language model trying to predict the next word based on the previous ones. If we are trying to predict the last word in “the clouds are in the sky,” we don’t need any further context – it’s pretty obvious the next word is going to be sky. In such cases, where the gap between the relevant information and the place that it’s needed is small, RNNs can learn to use the past information. But there are also cases where we need more context. Consider trying to predict the last word in the text “I grew up in France… I speak fluent French.” Recent information suggests that the next word is probably the name of a language, but if we want to narrow down which language, we need the context of France, from further back. It’s entirely possible for the gap between the relevant information and the point where it is needed to become very large. Unfortunately, as that gap grows, RNNs become unable to learn to connect the information. In theory, RNNs are absolutely capable of handling such “long-term dependencies.” A human could carefully pick parameters for them to solve toy problems of this form. Sadly, in practice

#### Annotation 4963736358156

 #machine-learning #nlp #unfinished As the gap between the relevant information and the point where it is needed becomes very large, RNNs become unable to learn to connect the information.

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mation suggests that the next word is probably the name of a language, but if we want to narrow down which language, we need the context of France, from further back. It’s entirely possible for <span>the gap between the relevant information and the point where it is needed to become very large. Unfortunately, as that gap grows, RNNs become unable to learn to connect the information. <span>

#### Original toplevel document

Olah-2015-Understanding_LSTM_Networks-colah,github,io
derstanding of the present frame. If RNNs could do this, they’d be extremely useful. But can they? It depends. Sometimes, we only need to look at recent information to perform the present task. <span>For example, consider a language model trying to predict the next word based on the previous ones. If we are trying to predict the last word in “the clouds are in the sky,” we don’t need any further context – it’s pretty obvious the next word is going to be sky. In such cases, where the gap between the relevant information and the place that it’s needed is small, RNNs can learn to use the past information. But there are also cases where we need more context. Consider trying to predict the last word in the text “I grew up in France… I speak fluent French.” Recent information suggests that the next word is probably the name of a language, but if we want to narrow down which language, we need the context of France, from further back. It’s entirely possible for the gap between the relevant information and the point where it is needed to become very large. Unfortunately, as that gap grows, RNNs become unable to learn to connect the information. In theory, RNNs are absolutely capable of handling such “long-term dependencies.” A human could carefully pick parameters for them to solve toy problems of this form. Sadly, in practice

#### Flashcard 4963996929292

Tags
#machine-learning #nlp
Question
What does AKBC stand for?
Automated Knowledge Base Construction

status measured difficulty not learned 37% [default] 0

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As AKBC is an active area of research, we hope to share our experiences and feedback with the AKBC community [22], highlighting areas for future investigation and improvement, from the perspect

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#### Annotation 4965667573004

 #9 #Certificats #Cours #Facultaires #Légale #Médecine L'ITT est utilisée aussi bien en matière de violences volontaires qu'en cas d'accidents (violences involontaires)

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#### Annotation 4965668621580

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Définition de l'ITT au sens du Code pénal : Il s'agit d'une perte majeure d'autonomie, c'est-à-dire l'impossibilité pour une victime d'effectuer seule les actes ordinaires et essentiels de la vie quotidienne. Attention, il n'existe aucun lien avec le travail professionnel, malgré l'ambigüité du terme (incapacité totale de travail), ce qui explique que l'on peut délivrer une ITT pénale à un enfant, à un retraité ou à un chômeur.

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#### Annotation 4965669670156

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Le tribunal qui jugera l'auteur des faits ainsi que la nature de la sanction pénale dépendent de la durée l'ITT. Cependant, si la Justice doit tenir compte de l'avis du médecin, le magistrat n'est jamais lié à cet avis, notamment parce qu'il dispose d'autres éléments, par exemple la récidive d'une infraction ou de très nombreuses circonstances aggravantes notamment liées à la qualité de la victime (encadré 3.2). Si le médecin constate chez la victime une situation qui pourrait être considérée comme une circonstance aggravante, il doit le mentionner sur le certificat

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#### Annotation 4965670718732

 #9 #Certificats #Cours #Facultaires #Légale #Médecine En cas de coups et blessures volontaires (agressions ; tableau 3.2) Si l'ITT au sens du Code pénal est strictement supérieure à huit jours, il s'agit d'un délit qui relève d'un jugement au tribunal correctionnel.Si l'ITT au sens du Code pénal est inférieure ou égale à huit jours, il s'agit d'une contravention qui relève d'un jugement au tribunal de police.Si l'ITT est nulle, il faut quand même l'écrire dans le certificat (« Il n'existe pas d'ITT au sens du Code pénal »), il s'agit également d'une contravention

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#### Annotation 4965671767308

 #9 #Certificats #Cours #Facultaires #Légale #Médecine En cas de coups et blessures involontaires (accidents) Le raisonnement est le même (qu'en cas de violence volontaire), mais ici la barrière juridique n'est plus de huit jours, elle est de trois mois : ITT strictement supérieure à trois mois : jugement au tribunal correctionnelITT strictement inférieure à trois mois : jugement au tribunal de police

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#### Annotation 4965672815884

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Quelle que soit l'ITT pénale proposée par le médecin, le magistrat peut décider de correctionnaliser (par exemple si la victime est vulnérable ou s'il y a récidive de l'infraction). L'infraction peut même relever des Assises (par exemple en cas de viol, de mutilation ou d'infirmité permanente). Ceci est de la responsabilité du procureur

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#### Annotation 4965673864460

 #9 #Certificats #Cours #Facultaires #Légale #Médecine On rappelle en effet qu'en civil (qui est le cadre judiciaire destiné à permettre une indemnisation sous forme de dommages et intérêts de la victime), la charge de la preuve incombe à la victime. Il faut donc bien intégrer l'importance d'une description exhaustive des lésions dans un certificat médical initial

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#### Annotation 4965675961612

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Points clés : Les constatations médicales initiales sont importantes.Il faut bien distinguer les allégations du patient des constatations médicales objectives.Le médecin doit décrire les lésions avec précision.Le certificat médical engage toujours la responsabilité du médecin rédacteur.L'incapacité totale de travail au sens du Code pénal (ITT pénale) conditionne en partie le tribunal compétent.

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#### Annotation 4965690903820

 #9 #Certificats #Cours #Facultaires #Légale #Médecine La définition des violences faites aux femmes adoptée par la France est celle de la convention européenne d'Istanbul (5 mai 2014) : « La violence à l'égard des femmes doit être comprise comme une violation des droits de l'homme et une forme de discrimination à l'égard des femmes et désigne tout acte de violence fondé sur le genre qui entraîne ou sont susceptibles d'entrainer pour les femmes des dommages ou souffrances de nature physique, sexuelles, psychologiques ou économiques y compris la menace de se livrer à de tels actes, la contrainte ou la privation arbitraire de liberté que ce soit dans la vie publique ou privée. »

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#### Annotation 4965691952396

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Violences conjugales (définition de l'OMS 2012) : « Adoption par le/la conjoint(e) d'un comportement qui cause un préjudice physique, sexuel ou psychologique, comme les actes d'agression physiques, les relations sexuelles forcées, la violence psychologique et tout autre acte de domination envers son/sa conjoint(e). »

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#### Annotation 4965693000972

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Violences conjugales : 95 % des victimes de violences conjugales sont des femmes.Chaque année, 220 000 femmes sont victimes de violences conjugales.En 2017, les décès survenus dans un contexte de violences conjugales ont concerné : 130 femmes21 hommes25 enfants mineurs.

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#### Annotation 4965694049548

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Le coût global des violences conjugales en France est estimé à plus de 3,6 milliards d'euros par an (soins, arrêt de travail, consommation médicamenteuse, décès, incarcération…).

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#### Annotation 4965695098124

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Les violences conjugales sont un délit, quelle que soit la durée de l'incapacité totale de travail (article 222-13 du Code pénal). Constituent une circonstance aggravante les violences ou harcèlements commis : « par le conjoint,le concubinou les partenaires lié(s) à la victime par un pacte civil de solidaritéou un ex-conjoint, un ex-concubin ou un ex-partenaire pacsé ».

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#### Annotation 4965696146700

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Violences conjugales : Poser systématiquement la question (tableau 3.3) Type de questions : Avez-vous déjà subi des violences ?Subissez-vous des violences sexuelles ?Comment cela se passe à la maison ?Comment se passent vos rapports intimes ?

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#### Annotation 4965697195276

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Repérer les situations à risques de violences conjugales : Situations liées au conjoint victime : GrossesseFemme jeune avec un faible niveau de scolarisationFemme étrangère sans professionHandicap physique ou mental Situations liées au conjoint agresseur : AddictionChômage Situations particulières du couple : Rupture, divorceDifficultés économiques

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#### Annotation 4965698243852

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Violences conjugales : Orienter la victime vers un réseau de partenaires professionnels et associatifs (3919 : Violences Femmes Info, numéro gratuit d'écoute et d'information anonyme et qui n'est pas repérable sur les factures et les téléphones)

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#### Annotation 4965699292428

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Violences conjugales : Le signalement aux autorités judiciaires : Avec l'accord de la victime, il est possible de signaler au procureur les faits de violences dont elle est victime (article 226-14 alinéa 2)Lorsque la femme est en situation de vulnérabilité et considérée comme hors d'état de se protéger (en raison d'un handicap physique ou psychique, d'une dépendance psychologique…), il est possible de signaler au procureur les violences qu'elle subit même sans consentement (article 226-14 du code pénal)

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#### Annotation 4965700341004

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Violences conjugales : Pointsclés : Les violences conjugales sont un délit et sont punies par la loi comme des violences aggravées en raison de la qualité de l'auteur.La lutte contre les violences conjugales est un enjeu majeur de santé publique.La grossesse est un moment de vulnérabilité pour la femme et une situation à risque de déclenchement ou d'aggravation des violences conjugales.Le repérage passe par le questionnement systématique sur l'existence de violences

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#### Annotation 4965719477516

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Refroidissement cadavérique : Plateau initial puis perte de 1 °C par heure jusqu'à l'atteinte de l'équilibre avec la température ambiante.Facteurs de variation : Température corporelle initialeTempérature ambiantePoidsHabillementPostureFacteurs météorologiques. Température rectale la plus utilisée

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#### Annotation 4965720526092

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Lividités Taches cutanées rouge-violacé secondaires à la transsudation du sang à travers les vaisseaux et à son déplacement sous l'effet de la pesanteur dans zones déclives. Elles sont absentes au niveau des zones d'appui

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#### Annotation 4965721574668

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Lividités Intérêts médico-légal : Aspect pouvant être influencé par la cause du décès (spoliation sanguine : lividités pâles et peu étendues ; intoxication au monoxyde de carbone : lividités rouge vif)Elles peuvent permettre de mettre en évidence un déplacement du corps (topographie incompatible avec la position du corps). Évolution (avec d'importantes variations) : Apparition : 45 minutes (plus ou moins 30 minutes)Intensité et étendue maximum : 9 heures et 30 minutes (plus ou moins 4 heures et 30 minutes)Disparition incomplète après retournement : 11 heures (plus ou moins 4 heures et 30 minutes)

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#### Annotation 4965722623244

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Rigidité : Contraction post-mortem des muscles striés et lisses consécutive à l'irréversibilité des liaisons actine-myosine secondaire à la chute de l'ATP.Elle débute au niveau des masséters en suivant une progression descendante et suit la même progression lors de sa résolution.Elle prédomine aux fléchisseurs aux membres supérieurs et aux extenseurs aux membres inférieurs

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#### Annotation 4965723671820

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Rigidité : Évolution (avec d'importantes variations) : Début : 3 heures (plus ou moins 30 minutes)Rigidité complète : 8 heures (plus ou moins 1 heure)Résolution : 76 heures (plus ou moins 32 heures). L'évolution peut être influencée par la température ambiante (apparition favorisée par la chaleur et inhibée par le froid, et inversement pour la résolution), l'humidité, l'âge et la constitution musculaire

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#### Annotation 4965724720396

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Certificat de décès - Modèle CERFA (papier) Recto partie supérieure : Partie administrative et nominative. Trois feuillets adressés : Àla mairie de la chambre funéraireÀ la chambre funéraireÀ la mairie du lieu du décès Intérêt : permet la délivrance du permis d'inhumer et de l'acte de décès par l'officier d'état civil

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#### Annotation 4965725768972

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Certificat de décès - Modèle CERFA (papier) Recto partie inférieure (figure 3.25) : Partie médicale et anonyme.Un feuillet diagnostic adressé au CepiDC Inserm (Centre d'épidémiologie sur les causes médicales de décès)

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#### Annotation 4965726817548

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Certificat de décès électronique Conditions d'utilisation : Décès certifiés en établissements de santé ou en établissements médico-sociaux : Données administratives pouvant être saisies par du personnel administratif habilité, tout en préservant la confidentialité des données (médicales) renseignées par le médecin Exercice libéral : Carte de professionnel de santé (CPS)Identifiant et mot de passe personnel obtenus auprès de l'Inserm. Il y a deux parties à renseigner : Un volet administratif imprimé et signé par le médecin et adressé aux mêmes destinataires que pour le support papierUn volet médical télétransmis en temps réel après cryptage à l'Inserm. Il est possible de préparer et d'enregistrer un certificat avant de revenir le valider dans un délai de 96 heures à compter de sa date de création et, en cas d'erreur, de corriger un certificat déjà validé dans un délai de 96 heures

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#### Annotation 4965727866124

 #9 #Certificats #Cours #Facultaires #Légale #Médecine La case de la rubrique « obstacle médico-légal à l'inhumation » est cochée en fonction de la forme médico-légale apparente de la mort : En cas de mort naturelle, c'est-à-dire de mort attendue correspondant à l'évolution naturelle d'une pathologie connue, elle ne doit pas être cochée En cas de mort violente (homicide volontaire ou involontaire, suicide, accident), elle doit être cochéeEn cas de mort suspecte (mort pour laquelle il n'est pas possible d'exclure l'intervention d'un tiers, mort subite ou inexpliquée), elle doit être cochéePar ailleurs, en cas de mort susceptible de poser un problème de responsabilité médicale et à chaque fois que le médecin n'est certain du caractère naturel du décès, elle doit être cochée.

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#### Annotation 4965729176844

 #9 #Certificats #Cours #Facultaires #Légale #Médecine La case de la rubrique « obligation de mise en bière immédiate » doit être cochée en cas : De maladie contagieuse : Rubrique « cercueil hermétique » en cas d'orthopoxviroses, de choléra, de charbon, de fièvres hémorragiques virales, pesteRubrique « cercueil simple » en cas de rage, tuberculose active, maladie infectieuse transmissible après avis du Haut Conseil de la Santé D'altération du corps

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#### Annotation 4965730225420

 #9 #Certificats #Cours #Facultaires #Légale #Médecine La case de la rubrique « obstacle au don du corps » doit être cochée en cas : D'obstacle médico-légal à l'inhumation De maladie contagieuse. La case de la rubrique « prélèvement en vue de rechercher la cause du décès » doit être cochée en cas de suspicion de maladie contagieuse faisant l'objet des rubriques « cercueil hermétique » et « cercueil simple », à la demande : Du médecin constatant le décèsDu préfet

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#### Annotation 4965754866956

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Conséquences d'un obstacle médico-légal : Obligation pour le médecin de se mettre en rapport avec le procureur de la République ou les services de police ou de gendarmerie.Possibilité de réquisition d'un médecin pour procéder à une levée de corps médico-légale.Possibilité de réquisition d'un ou de plusieurs médecins par le procureur de la République, un juge d'instruction ou un officier de police judiciaire pour réaliser une examen du corps ou une autopsie médico-légale auxquels l'entourage ne peut pas s'opposer.Impossibilité de : Réaliser les opérations funéraires figurant dans les autres rubriques, l'inhumation ou la crémationPrésenter le corps aux proches

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#### Annotation 4965755915532

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Lorsqu'une recherche médicale ou scientifique des causes du décès a été réalisée ou après une autopsie judiciaire, un volet médical complémentaire peut être rempli par le médecin ayant pratiqué l'examen complémentaire. Comme pour la partie médicale, il s'agit d'un document anonyme qui doit être transmis par voie électronique au CepiDC Inserm.

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#### Annotation 4965756964108

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Levée de corps médico-légale : Définition Il s'agit de l'examen d'un cadavre réalisé sur réquisition en lieu et place de sa découverte

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#### Annotation 4965758012684

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Opérations consécutives au décès : Trois conditions indispensables pour permettre leur réalisation Absence d'obstacle médico-légal à l'inhumation.Absence d'obligation de mise en bière immédiate en raison d'une maladie contagieuse ou d'un mauvais état du corps.Réalisation dans un délai maximum à compter du décès

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#### Annotation 4965759061260

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Transport du corps sans mise en bière vers son domicile, la résidence d'un membre de sa famille ou une chambre funéraire Conditions : Demande d'une personne ayant qualité pour pourvoir aux funérailles ou, si impossibilité de joindre une personne ayant cette qualité, demande de la personne chez qui a eu lieu le décès ou demande du directeur de l'établissement de santé ou a eu lieu le décès Accord, le cas échéant, du directeur de l'établissement de soins et du médecin du service ou du médecin ayant constaté le décès (lorsque le décès n'est pas survenu dans un établissement de santé) Déclaration préalable du transport au maire du lieu de dépôt du corps. Délai : au maximum 48 heures. Dans le cas d'un obstacle médico-légal, la réquisition d'un officier de police judiciaire vaut demande de transport vers la chambre funéraire

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#### Annotation 4965760109836

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Don du corps : Conditions : Déclaration écrite, datée et signée de l'intéressé avec copie adressée à la faculté de médecine à laquelle le corps est léguéMineur ou majeur sous tutelle ne pouvant effectuer cette démarcheDélivrance par la faculté à l'intéressé d'une carte de donneur qu'il s'engage à porter sur lui (carte permettant au moment du décès le transfert du corps)Transport sans mise en bière du corps vers la faculté après déclaration au maire du lieu de conservation du corps Délai : au maximum 48 heures

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#### Annotation 4965761158412

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Transport du corps d'une personne vers un établissement de santé pour réaliser des prélèvements : Conditions : Demande du directeur de l'établissement de santé où la personne est décédée ou de toute personne ayant qualité pour pourvoir aux funéraillesDéclaration préalable au transport au maire du lieu de décès ou de dépôt du corps. Délai : au maximum 48 heures et 72 heures lorsque l'autopsie médicale a pour objectif de diagnostiquer une maladie de Creutzfeld-Jakob.

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#### Annotation 4965762206988

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Soins de conservations : Conditions : Expression écrite des dernières volontés du défunt ou d'une personne ayant qualité pour pourvoir aux funéraillesDéclaration à la mairie indiquant le produit utilisé, le lieu et l'heure de l'opération ainsi que le nom et l'adresse de la personne ou de l'entreprise qui y procédera

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#### Annotation 4965763255564

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Transport de corps après mise en bière : Conditions : Nécessité de mise en bière immédiateEn cas de mort naturelle : À l'intérieur du territoire métropolitain ou d'un département d'outre-mer : déclarations à l'officier d'état civil de la commune du lieu de fermeture du cercueilEn dehors du territoire métropolitain ou d'un département d'outre-mer : autorisation par le préfet du département ou a eu lieu la fermeture du cercueilEn cas de réalisation d'une autopsie médico-légale : autorisation de l'autorité judiciaire et de l'officier d'état civil de la commune du lieu de fermeture du cercueil. Délai : pas de délai (sauf le délai d'inhumation dans les six jours suivant le décès)

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#### Annotation 4965764304140

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Inhumation Conditions : En cas de mort naturelle : Permis d'inhumer délivré par l'officier d'état civil : Décès ayant eu lieu en France métropolitaine : 24 heures au moins et six jours au plus après le constat du décèsDécès ayant eu lieu à l'étranger ou dans un territoire d'outre-mer : six jours au plus après le constat de décès En cas d'obstacle médico-légal à l'inhumation : Permis d'inhumer délivré par l'autorité judiciaire Inhumation dans les six jours, sauf dérogation préfectorale.

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#### Annotation 4965765352716

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Crémation Conditions : En cas de mort naturelle : Autorisation délivrée par l'officier d'état civil après contrôle des dernières volontés écrites de la personne et dans un délai identique à celui de l'inhumation En cas d'obstacle médico-légal à l'inhumation : Autorisation délivrée par l'autorité judiciaire

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#### Annotation 4965766401292

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Les enfants nés vivants et viables décédés avant que la naissance ait été déclarée à l'état civil Établissement par l'officier d'état civil d'un acte de naissance et d'un acte de décès sur production d'un certificat médical mentionnant que l'enfant est né vivant et viable et précisant les jours et heures de sa naissance et de son décès : L'enfant a une personnalité juridique avec les conséquences en découlantIl est inscrit sur le livret de famille

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#### Annotation 4965767449868

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Les enfants nés vivants mais non viables et décédés et les enfants mort- nés : Possibilité d'établissement par l'officier d'état civil d'un acte d'enfant sans vie à la demande des parents ou de l'un d'eux. Cet acte d'enfant sans vie est délivré sur production d'un certificat attestant d'un accouchement d'un enfant né sans vie : l'enfant y est décrit « mort-né » ou « né vivant mais non viable » L'enfant n'a pas de personnalité juridiqueIl peut être inscrit sur les registres de décès et sur le livret de famille

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#### Annotation 4965768498444

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Points clés : Le diagnostic de la mort repose sur la constatation des signes négatifs de la vie et des signes positifs précoces de la mort permettant d'évaluer approximativement le délai post-mortem.Depuis le 1 er janvier 2018, la certification légale des décès est la certification électronique comportant un volet administratif, un volet médical modifiable durant 96 heures et un volet médical complémentaire renseigné en cas d'autopsie.Depuis le 1 er janvier 2018, les soins de conservation peuvent être réalisés chez des sujets séropositifs au HIV et à l'hépatite B

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#### Annotation 4965795499276

 #knowledge-base-construction #machine-learning #unfinished For mentions, Fonduer builds a Bi-LSTM to get the textual features of the mention from both directions of sentences containing the candidate.

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#### Annotation 4965797072140

 #knowledge-base-construction #machine-learning #unfinished For sentence s i containing the i th mention in the document, the textual features h ik of each word w ik are encoded by both forward (defined as superscript F in equations) and backward (defined as superscript B ) LSTM, which summarizes information about the whole sentence with a focus on w ik . This takes the structure: h F ik = LSTM(h F i(k−1) , Φ(s i , k)) h B ik = LSTM(h B i(k+1) , Φ(s i , k)) h ik = [h F ik , h B ik ] where Φ(s i , k) is the word embedding [ 40 ], which is the representa- tion of the semantics of the k th word in sentence s i

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#### Annotation 4965798645004

 #knowledge-base-construction #machine-learning #unfinished Then, the textual feature representation for a mention, t i , is calcu- lated by the following attention mechanism to model the importance of different words from the sentence s i and to aggregate the feature representation of those words to form a final feature representation, u ik = tanh(W w h ik + b w ) α ik = exp(u T ik u w ) Σ j exp(u T ij u w ) t i = Σ j α ij u ij where W w , u w , and b are parameter matrices and a vector. u ik is the hidden representation of h ik , and α ik is to model the importance of each word in the sentence s i .

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#### Annotation 4965800217868

 #knowledge-base-construction #machine-learning #unfinished Special candidate markers (shown in red in Figure 5) are added to the sentences to draw attention to the candidates themselves. Finally, the textual features of a candidate are the concatenation of its mentions’ textual features [t 1 , . . . , t n ].

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#### Annotation 4965801790732

 #knowledge-base-construction #machine-learning #unfinished Structural features. These provide signals intrinsic to a docu- ment’s structure. These features are dynamically generated and allow Fonduer to learn from structural attributes, such as parent and sibling relationships and XML/HTML tag metadata found in the data model (shown in yellow in Figure 5). The data model also allows Fonduer to track structural distances of candidates, which helps when a candidate’s mentions are visually distant, but structurally

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#### Annotation 4965804936460

 #empresa #investimentos-inteligentes Se você pensar em montar uma empresa e entendê-la como um investimento que precisará de um período de maturação, estará no caminho certo. Se tiver planos de montar uma empresa porque está precisando tirar dinheiro de algum lugar para se manter, é bem provável que seu negócio não venha a contar com o fôlego necessário para crescer e que, por isso, mingue à sombra do sucesso de outros empreendedores. Pensando assim, seu negócio será apenas um meio trabalhoso de perder seu dinheiro.

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#### Annotation 4965806509324

 #9 #Certificats #Cours #Facultaires #Légale #Médecine En 2015, les premiers prélèvements sur donneurs décédés après circulatoire suite à une limitation ou à un arrêt des thérapeutiques ont été réalisés

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#### Annotation 4965810441484

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Lois de bioéthique du 6 août 2004 Création de l'Agence de biomédecine.Confirmation du principe de consentement présumé.Précision des principes d'utilisation des produits du corps.Prise en compte du respect dû au corps : obligation de « s'assurer de la meilleure restauration possible du corps ».Délai de révision de la loi à cinq ans.

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#### Annotation 4965813062924

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Lois de bioéthique du 7 juillet 2011 Évolution du recueil du positionnement du défunt : La loi du 11 août 2016 a renforcé le principe de la présomption de consentement Une personne peut s'opposer au prélèvement en s'inscrivant sur le registre national des refus Une personne peut également exprimer son refus par écrit et confier ce document à un proche. Ce document est daté et signé par son auteur dûment identifié par l'indication de ses nom, prénom, date et lieu de naissance Lorsqu'une personne, bien qu'en état d'exprimer sa volonté, est dans l'impossibilité d'écrire et de signer elle-même ce document, elle peut demander à deux témoins d'attester que le document qu'elle n'a pu rédiger elle-même est l'expression de sa volonté libre et éclairée. Ces témoins indiquent leur nom et qualité et leur attestation est jointe au document exprimant le refus Un proche de la personne décédée peut faire valoir le refus de prélèvement d'organes que cette personne a manifesté expressément de son vivant. Ce proche ou l'équipe de coordination hospitalière de prélèvement transcrit par écrit ce refus en mentionnant précisément le contexte et les circonstances de son expression. Ce document est daté et signé par le proche qui fait valoir ce refus et par l'équipe de coordination hospitalière de prélèvement. Les proches doivent être informés de la finalité des prélèvements

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#### Annotation 4965814897932

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Don d'un majeur Le don nécessite : Le consentement du donneur après une information claire, loyale, éclairée, appropriée à l'état du patient et à sa compréhensionL'autorisation par un comité d'experts qui : Apprécie la justification médicale et les risques encourus par le donneurInforme le donneurVérifie l'absence de pression sociale ou psychologique

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#### Annotation 4965815946508

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Comité d'experts Cinq membres : trois médecins, un psychologue, un SHS.Les experts sont désignés pour trois ans.Le comité ne motive pas ses décisions, il n'y a pas d'appel possible.

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#### Annotation 4965816995084

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Recueil du consentement Le consentement est recueilli par le président du tribunal de grande instance (TGI).Dans une situation d'urgence, il peut être recueilli par le procureur de la République : Vérification des identitésVérification des liens entre receveur et donneurInformation sur les risques et les conséquences

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#### Annotation 4965818043660

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Interdictions Donneur vivant mineur : Prélèvements d'organes interditsPrélèvements de cellules hématopoïétiques (moelle ou sang périphérique) ou de sang autorisés dans certains cas. Donneur vivant majeur protégé : Prélèvements d'organes interditsPrélèvements de cellules hématopoïétiques autorisés dans certains casPrélèvements de sang interdits.

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#### Annotation 4965819092236

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Don de moelle osseuse Don familial d'un majeur : Frère et sœur : Pas de nécessité d'autorisation du comité d'expertsExpression du consentement devant le président du TGI ou en cas d'urgence devant le procureur de la République. Don anonyme d'un majeur : registre France Greffe de moelle : Donneur : 18 à 51 ansÉvaluation médicale/profil HLAAttente d'être contactéExpression du consentement devant le président du TGI et autorisation par un comité d'experts ou en cas d'urgence par le procureur de la République.

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#### Annotation 4965820402956

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Don de sang Donneur majeur : principe d'anonymat : « Le receveur ne peut connaître l'identité du donneur, ni le donneur celle du receveur. Aucune information permettant d'identifier à la fois celui qui a fait don de son sang et celui qui l'a reçu ne peut être divulguée. Il ne peut être dérogé à ce principe d'anonymat qu'en cas de nécessité thérapeutique. » Donneur majeur protégé : le don de sang est strictement interdit (article L.1221-5 du Code de la santé publique). Donneur mineur : prélèvement interdit en principe (article L.1221-5 du Code de la santé publique), sauf en présence d'une urgence thérapeutique (article L.1241-5 alinéa 2 du Code de la santé publique) : Consentement des titulaires de l'autorité parentale et du mineur par écritPas de nécessité d'expression du consentement devant le président du TGI

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#### Annotation 4965821451532

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Don de spermatozoïdes et d'ovocytes Tout homme et femme âgé de 18 à 37 ans.Consentement : Signature d'un consentement sur lequel le donneur peut revenir à tout moment jusqu'à l'utilisation des gamètesLorsque le donneur vit en coupe, signature du consentement par l'autre membre du couple. Gratuité du don avec prise en charge des frais occasionnés par le don.Don anonyme

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#### Flashcard 4965840325900

Question
Plasmid Encoded
• Enterotoxigenic Escherichia coli

• Extraintestinal E. coli

• Shigella spp. and enteroinvasive E. coli

• Yersinia spp

• Bacillus anthracis

• Staphylococcus aureus

• Clostridium tetani

status measured difficulty not learned 37% [default] 0

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#### Flashcard 4965841374476

Tags
#MLBook #machine-learning
Question
Describe a proper way of dealing with a categorical feature "colors" with three possible values: “red,” “yellow,” “green” when the learning algorithm only works with numerical feature vectors.

Some learning algorithms only work with numerical feature vectors. When some feature in your dataset is categorical, like “colors” or “days of the week,” you can transform such a categorical feature into several binary ones.

If your example has a categorical feature “colors” and this feature has three possible values: “red,” “yellow,” “green,” you can transform this feature into a vector of three numerical values:
\begin{align*} \textrm{red} & = [1, 0, 0] \\ \textrm{yellow} & = [0, 1, 0] \\ \textrm{green} & = [0, 0, 1] \end{align*} (1)

By doing so, you increase the dimensionality of your feature vectors. You should not transform red into 1, yellow into 2, and green into 3 to avoid increasing the dimensionality because that would imply that there’s an order among the values in this category and this specific order is important for the decision making. If the order of a feature’s values is not important, using ordered numbers as values is likely to confuse the learning algorithm, because the algorithm will try to find a regularity where there’s no one, which may potentially lead to overfitting.

status measured difficulty not learned 37% [default] 0

#### Parent (intermediate) annotation

Open it
Some learning algorithms only work with numerical feature vectors. When some feature in your dataset is categorical, like “colors” or “days of the week,” you can transform such a categorical feature into several binary ones. If your example has a categorical feature “colors” and this feature has three possible values: “red,” “yellow,” “green,” you can transform this feature into a vector of three numerical values: \begin{align*} \textrm{red} & = [1, 0, 0] \\ \textrm{yellow} & = [0, 1, 0] \\ \textrm{green} & = [0, 0, 1] \end{align*} (1) By doing so, you increase the dimensionality of your feature vectors. You should not transform red into 1, yellow into 2, and green into 3 to avoid increasing the dimensionality because that would imply that there’s an order among the values in this category and this specific order is important for the decision making. If the order of a feature’s values is not important, using ordered numbers as values is likely to confuse the learning algorithm, because the algorithm will try to find a regularity where there’s no one, which may potentially lead to overfitting.

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#### Annotation 4965846617356

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Définition et contexte Mort encéphalique : destruction irréversible de l'encéphale et perfusion des autres organes maintenue. Causes principales de la mort encéphalique : Accidents vasculaires cérébraux : 56,3 % des donneursAccidents de la circulation et autres traumatismes : 21 % des donneursAnoxies cérébrales (pendaisons, noyades, etc.) : 21 % des donneursIntoxications

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#### Annotation 4965849238796

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Critères cliniques Abolition des réflexes du tronc cérébral.Absence d'activité motrice.Absence de ventilation spontanée Critères paracliniques Absence de respiration spontanée vérifiée par une épreuve d'hypercapnie = absence de mouvements inspiratoires malgré une augmentation de la capnie objectivée par des gaz du sang : PaCO2 > 60 mm Hg ETAbsence d'activité cérébrale vérifiée par : 2 EEG nuls et aréactifs effectués à 4 heures d'intervalle avec une durée d'enregistrement de 30 minutes minimum, une amplification maximum, au cours d'un arrêt des traitements sédatifs, anticonvulsivants OUUne angiographie cérébrale (angioscanner) objectivant l'arrêt de la circulation artérielle et veineuse encéphalique (figure 3.28)

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#### Annotation 4965850287372

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Don en mort encéphalique : Constat et procès-verbal de décès Le constat et procès-verbal de décès est établi en trois exemplaires par deux médecins qui ne doivent pas appartenir à la même unité fonctionnelle (UF) ou au même service que les médecins réalisant le prélèvement ou la greffe d'organes.Le constat est établi en trois exemplaires : un pour chaque médecin et un conservé par l'établissement dans lequel le prélèvement a lieu

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#### Annotation 4965852122380

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Conditions d'inscription sur le registre national automatisé des refus : Avoir plus de 13 ans.L'inscription se fait auprès de l'Agence de biomédecine (inscription en ligne possible : www.registrenationaldesrefus.fr) Le refus peut être précisé : Refus de prélèvement pour une greffe d'organes et de tissusRefus de prélèvement pour la recherche scientifiqueRefus d'autopsieRefus de prélèvement d'un organe particulier, tout en acceptant le prélèvement des autres organes. Le refus peut être révoqué à tout moment

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#### Annotation 4965855792396

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Information de la famille sur la finalité des prélèvements Lors de cette rencontre, si la personne décédée ne s'est pas inscrite sur le registre des refus, un membre de la famille peut faire part de l'opposition de son proche au prélèvement. Ce témoignage doit être écrit, retranscrire le contexte et les circonstances de recueil de l'opposition et être daté et signé par le proche.

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#### Annotation 4965857627404

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Donneur en état de mort cérébrale Cas particulier Donneur mineur : consentement écrit de chacun des titulaires ou du tuteur (autorisation écrite). En cas d'impossibilité de consulter un des titulaires de l'autorité parentale, un seul suffit. Donneur majeur protégé : consentement écrit du tuteur.

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#### Annotation 4965859724556

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Donneur en ACR persistant : PV de décès : un seul médecin.Le receveur consent à recevoir un greffon à critères élargis (il reste inscrit sur la liste nationale)

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#### Annotation 4965860773132

 #9 #Certificats #Cours #Facultaires #Légale #Médecine Don d'organe en ACR persistant : Procédure La durée d'asystolie sans massage efficace doit être inférieure à : 30 minutes pour le prélèvement des reins15 minutes pour le prélèvement du foie. La durée d'ischémie chaude doit être inférieure à 120 minutes (150 minutes si une machine à masser est utilisée). ACR persistant malgré une réanimation bien conduite. Arrêt du massage pendant 5 minutes et vérification des critères cliniques (tracé ECG continu de 5 minutes).Reprise du massage et mise en place d'une sonde de Gillot (refroidissement in situ) ou d'une circulation normothermique (CEC) sous-diaphragmatique pour perfusion des organes.Ne pas dépasser 180 minutes entre cette perfusion et le prélèvement.Ischémie froide (jusqu'à la greffe) inférieure à 18 heures

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#### Annotation 4965862083852

 #9 #Certificats #Cours #Facultaires #Légale #Médecine L'existence d'un obstacle médico-légal à l'inhumation n'est pas une contre-indication absolue à un éventuel prélèvement multi-organes (PMO) : L'équipe de coordination des prélèvements contacte le magistrat en charge de l'enquêteUn examen externe est effectué avant le PMO par un médecin légiste sur réquisition. Le médecin légiste peut être présent pendant le prélèvementUne autopsie médico-légale est réalisée ou non après le PMO

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#### Annotation 4965863132428

 #MLBook #machine-learning #test-set #training-set #validation-set Once you have got your annotated dataset, the first thing you do is you shuffle the examples and split the dataset into three subsets: training, validation, and test.

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Once you have got your annotated dataset, the first thing you do is you shuffle the examples and split the dataset into three subsets: training, validation, and test. The training set is usually the biggest one; you use it to build the model. The validation and test sets are roughly the same sizes, much smaller than the size of the training set. The

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#MLBook #machine-learning #test-set #training-set #validation-set
Question
Once you have got your annotated dataset, the first thing you do is you shuffle the examples and split the dataset into three subsets: [...].
training, validation, and test

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Once you have got your annotated dataset, the first thing you do is you shuffle the examples and split the dataset into three subsets: training, validation, and test.

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#### Annotation 4965870472460

 #MLBook #holdout-sets #machine-learning #test-set #training-set #validation-set The training set is usually the biggest one; you use it to build the model. The validation and test sets are roughly the same sizes, much smaller than the size of the training set. The learning algorithm cannot use examples from these two subsets to build the model. That is why those two sets are often called holdout sets.

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Once you have got your annotated dataset, the first thing you do is you shuffle the examples and split the dataset into three subsets: training, validation, and test. The training set is usually the biggest one; you use it to build the model. The validation and test sets are roughly the same sizes, much smaller than the size of the training set. The learning algorithm cannot use examples from these two subsets to build the model. That is why those two sets are often called holdout sets.

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#### Flashcard 4965872045324

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#MLBook #holdout-sets #machine-learning #test-set #training-set #validation-set
Question
The [...] is usually the biggest one; you use it to build the model. The validation and test sets are roughly the same sizes, much smaller than the size of the training set. The learning algorithm cannot use examples from these two subsets to build the model. That is why those two sets are often called holdout sets.
training set

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The training set is usually the biggest one; you use it to build the model. The validation and test sets are roughly the same sizes, much smaller than the size of the training set. The learning algorith

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#### Flashcard 4965874404620

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#MLBook #holdout-sets #machine-learning #test-set #training-set #validation-set
Question
The training set is usually the biggest one; you use it to build the model. The [...] are roughly the same sizes, much smaller than the size of the training set. The learning algorithm cannot use examples from these two subsets to build the model. That is why those two sets are often called holdout sets.
validation and test sets

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The training set is usually the biggest one; you use it to build the model. The validation and test sets are roughly the same sizes, much smaller than the size of the training set. The learning algorithm cannot use examples from these two subsets to build the model. That is why those two s

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#MLBook #holdout-sets #machine-learning #test-set #training-set #validation-set
Question
The training set is usually the biggest one; you use it to build the model. The validation and test sets are roughly the same sizes, much smaller than the size of the training set. The learning algorithm cannot use examples from these two subsets to build the model. That is why those two sets are often called [...].
holdout sets

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the same sizes, much smaller than the size of the training set. The learning algorithm cannot use examples from these two subsets to build the model. That is why those two sets are often called <span>holdout sets. <span>

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#MLBook #machine-learning #prediction #test-set #training-set #validation-set
Question
What is the reason to have three sets and not one in machine learning?
You may wonder, what is the reason to have three sets and not one. The answer is simple: when we build a model, what we do not want is for the model to only do well at predicting labels of examples the learning algorithms has already seen. A trivial algorithm that simply memorizes all training examples and then uses the memory to “predict” their labels will make no mistakes when asked to predict the labels of the training examples, but such an algorithm would be useless in practice. What we really want is a model that is good at predicting examples that the learning algorithm didn’t see: we want good performance on a holdout set.

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You may wonder, what is the reason to have three sets and not one. The answer is simple: when we build a model, what we do not want is for the model to only do well at predicting labels of examples the learning algorithms has already seen. A trivial algorithm that simply memorizes all training examples and then uses the memory to “predict” their labels will make no mistakes when asked to predict the labels of the training examples, but such an algorithm would be useless in practice. What we really want is a model that is good at predicting examples that the learning algorithm didn’t see: we want good performance on a holdout set.

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#MLBook #machine-learning #weight
Question
Some algorithms, like SVM, allow the data analyst to provide weightings for each class. These weightings influence how the decision boundary is drawn. If the weight of some class is high, [...].
the learning algorithm tries to not make errors in predicting training examples of this class (typically, for the cost of making an error elsewhere)

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span> Some algorithms, like SVM, allow the data analyst to provide weightings for each class. These weightings influence how the decision boundary is drawn. If the weight of some class is high, <span>the learning algorithm tries to not make errors in predicting training examples of this class (typically, for the cost of making an error elsewhere). <span>

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#### Annotation 4965899570444

 #10 #Cours #Facultaires #Légale #Médecine #Sexuelle #Violence Le Code pénal distingue : Le viol quand il y a pénétrationLes autres agressions sexuelles : Les attouchements : contact sexuel sans pénétrationLes exhibitions sexuelles (offense sexuelle de nature visuelle sans contact)Le harcèlement sexuelLes atteintes sexuelles sans violence, menace, contrainte ou surprise sur les mineurs

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#### Annotation 4965900619020

 #10 #Cours #Facultaires #Légale #Médecine #Sexuelle #Violence Le viol est un crime relevant de la cour d'assises, avec de possibles peines de prison, voire une réclusion criminelle pouvant aller jusqu'à perpétuité s'il y a eu torture et actes de barbarie

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#### Annotation 4965901667596

 #10 #Cours #Facultaires #Légale #Médecine #Sexuelle #Violence Les sanctions sont aggravées dans certains cas définis par le Code pénal : Vulnérabilité de la victimeÂge de la victimeAuteur ayant autoritéUsage d'une armeAgression en bandeGraves conséquences pour la victime (mutilation et infirmité permanente)

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#### Annotation 4965903502604

 #10 #Cours #Facultaires #Légale #Médecine #Sexuelle #Violence Violence sexuelle Il existe deux cas de figure possibles pour la réalisation de cet examen : Consultation sur réquisition de justice (procureur, officier de police judiciaire) voire ordonnance de juge d'instruction : Il s'agit d'une situation de dérogation au secret professionnel.Le rapport médico-légal et les prélèvements doivent être transmis à l'autorité requérante.Les prélèvements sont scellés immédiatement par l'officier de police judiciaire (garantie juridique).Cette voie doit être privilégiée pour éviter bien des déconvenues sur un plan juridique Consultation à la demande de la victime (refus de la victime d'effectuer un signalement et d'informer la justice) : c'est une consultation simple, avec dérogation facultative au secret professionnel, ce qui veut dire que le médecin peut informer le procureur de la République des faits, mais uniquement avec l'accord de la victime majeure. Si elle refuse, il n'y aura pas de signalement à la justice et pas de garantie juridique pour les prélèvements puisqu'ils ne seront pas scellés et ne seront pas remis à la justice.Mais le médecin doit quand même prendre en charge la patiente sur le plan médical.

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#### Annotation 4965904551180

 #10 #Cours #Facultaires #Légale #Médecine #Sexuelle #Violence Violence sexuelle Conditions médicales idéales pour réaliser cet examen Il est recommandé de pratiquer cet examen en présence d'une tierce personne, par exemple un gynécologue pour la prise en charge médicale parallèle, voire un IDE ou autre.Les conditions matérielles doivent être parfaites et l'examen fait de préférence dans un centre hospitalier équipé et habitué à ce type de prise en charge (cellule d'accueil des victimes d'agression sexuelle obligatoire dans les CHU 24 heures sur 24)

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#### Annotation 4965905599756

 #10 #Cours #Facultaires #Légale #Médecine #Sexuelle #Violence La déchirure traumatique est définie sur un plan médico-légal par une encoche complète qui atteint le bord d'insertion vaginal de l'hymen

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#### Annotation 4965906648332

 #10 #Cours #Facultaires #Légale #Médecine #Sexuelle #Violence Violence sexuelle - Examen Hyménéal Pour cela, on introduit une sonde à ballonnet derrière la membrane hyménéale, avant de gonfler le ballon 15 ml. On tire ensuite la sonde délicatement vers l'avant. Ceci permet de voir : Une défloration ancienne : déchirure non hémorragique et cicatrisée, atteignant la paroi vaginale. En cas de rapports répétés, et surtout après un accouchement, il ne persiste que des résidus hyménéaux, ou des caroncules myrtiformes Une défloration récente : déchirure hyménéale atteignant la paroi vaginale, plus ou moins hémorragique, le plus souvent située à 5 heures ou à 7 heures sur un cadran horaire Un hymen intact (figures 4.1 et 4.2) : absence de défloration ancienne ou récente visible. Il faut alors mesurer le diamètre maximal de l'orifice hyménéal, c'est-à-dire le diamètre maximum de l'orifice visible avec le ballon. Certains hymens peuvent se dilater de façon importante tout en restant intacts

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#### Annotation 4965907696908

 #10 #Cours #Facultaires #Légale #Médecine #Sexuelle #Violence Examen du vagin, des culs-de-sac et du col au spéculum Cet examen est à réaliser s'il y a eu des rapports sexuels antérieurs (en l'absence de rapports sexuels antérieurs, on peut utiliser un spéculum de vierge)

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#### Annotation 4965908745484

 #10 #Cours #Facultaires #Légale #Médecine #Sexuelle #Violence Proposer une anuscopie voire une rectoscopie si besoin (à adapter aux dires de la victime et à ne réaliser que s'il y a un doute sur la pénétration anale)

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#### Annotation 4965909794060

 #10 #Cours #Facultaires #Légale #Médecine #Sexuelle #Violence Violence sexuelle Prélèvements à visée médico-légale Ces prélèvements doivent être réalisés dans la mesure du possible dans le cadre d'une réquisition judiciaire.Ils nécessitent une garantie : Scientifique : conditions correctes de prélèvement. Les prélèvements doivent être : Numérotés et localisésSéchésConservés à l'abri de la lumièreCongelés à –20 °C s'ils ne sont pas utilisés dans les trois jours (sinon, les garder au réfrigérateur) Juridique : Apposition immédiate de scellésFaire les prélèvements en double exemplaire pour la contre-expertise éventuelle.

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Tags
#L1-regularization #L2-regularization #MLBook #feature-selection #machine-learning #sparse-model
Question
Discuss about L1 and L2 regularization characteristics.
In practice, L1 regularization produces a sparse model, a model that has most of its parameters (in case of linear models, most of $$w^{(j)}$$) equal to zero, provided the hyperparameter $$C$$ is large enough. So L1 makes feature selection by deciding which features are essential for prediction and which are not. That can be useful in case you want to increase model explainability. However, if your only goal is to maximize the performance of the model on the holdout data, then L2 usually gives better results. L2 also has the advantage of being differentiable, so gradient descent can be used for optimizing the objective function.

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In practice, L1 regularization produces a sparse model, a model that has most of its parameters (in case of linear models, most of $$w^{(j)}$$) equal to zero, provided the hyperparameter $$C$$ is large enough. So L1 makes feature selection by deciding which features are essential for prediction and which are not. That can be useful in case you want to increase model explainability. However, if your only goal is to maximize the performance of the model on the holdout data, then L2 usually gives better results. L2 also has the advantage of being differentiable, so gradient descent can be used for optimizing the objective function.

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#### Annotation 4965911891212

 #10 #Cours #Facultaires #Légale #Médecine #Sexuelle #Violence Violence sexuelle Prélèvements sur la victime : À partir des écouvillons : réalisation de multiples écouvillons, en nombre pair : vulve, vagin, culs de sac, col, face interne des cuisses, anus, interstices dentaires, etc., pour identifier l'ADN de l'auteur.Faire un prélèvement parallèle de sang de victime pour comparer le matériel génétique.Faire également : Un grattage des ongles de la victime (ou les couper) pour identifier l'ADN de l'auteurUn recueil de poils étrangers à la victime (pouvant appartenir à l'agresseur)Un recueil des taches sur les vêtements

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#### Annotation 4965912939788

 #10 #Cours #Facultaires #Légale #Médecine #Sexuelle #Violence Prélèvements sur l'auteur supposé pour comparer avec les résultats identifiés sur la victime (avec son accord) Réalisation d'écouvillons sur le gland du suspect rapidement après les faits : mise en évidence de cellules vaginales (microscopie), et ADN provenant de la victime dans ces cellules.Grattage des ongles du suspect : ADN provenant de la victime.Prélèvement de sang pour obtenir le profil ADN de la personne ou un écouvillon buccal

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#### Annotation 4965913988364

 #10 #Cours #Facultaires #Légale #Médecine #Sexuelle #Violence Recherche de toxiques sur la victime (dans un cadre de suspicion de soumission chimique) Définition de la soumission chimique : fait de donner volontairement à une victime un produit toxique (généralement un psychotrope) dans un but criminel (viol) ou délictuel (vol), ou simplement afin d'en tirer un bénéfice (parents donnant des psychotropes à un nouveau-né parce qu'il pleure la nuit). Certains cas se terminent par la mort de la victime, même si ce n'est pas le but recherché

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#### Annotation 4965915823372

 #10 #Cours #Facultaires #Légale #Médecine #Sexuelle #Violence Violence sexuelle Produits utilisés : Les produits en cause (habituellement incorporés dans les boissons à l'insu des victimes) peuvent être : Les benzodiazépines (surtout à demi-vie courte), les hypnotiques (surtout la zopiclone, le zolpidem et le flunitrazépam) ;L'alcoolLe mélange d'alcool et de psychotropesLes stupéfiants et les hallucinogènes ;Plus rarement les anesthésiques comme l'acide gamma-hydroxybutyrique, l'halothane, le protoxyde d'azote, les neuroleptiques, un anti H1 type alimémazine

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#### Annotation 4965918182668

 #10 #Cours #Facultaires #Légale #Médecine #Sexuelle #Violence Les cheveux doivent être prélevés en quantité suffisante (de la dimension du diamètre d'un crayon) et orientés (repérer la racine), cette orientation permettant un diagnostic chronologique. Les cheveux ne sont pas prélevés en urgence : il faut attendre qu'ils poussent pour contenir les produits toxiques éventuellement administrés lors de l'agression.

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#### Annotation 4965919231244

 #10 #Cours #Facultaires #Légale #Médecine #Sexuelle #Violence Violence sexuelle Prélèvements à visée médicale Recherche de MST (écouvillons vaginaux pour bactériologie : écouvillon endocol, cul de sac vaginal), prélèvement pour HSV, Chlamydia trachomatis.Frottis éventuel.BHCG immédiatement et à 15 jours.Recherche de pathologie infectieuse : TPHA-VDRL immédiatement et à un moisSérologies hépatite B et C immédiatement et à un moisChlamydia trachomatis immédiatement et à un moisSérologie mycoplasme immédiatement et à un moisSérologie herpès 1 et 2 immédiatement et à un mois.HIV 1 et 2 immédiatement, à un mois et à trois mois

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Tags
#MLBook #machine-learning #vector-function
Question
Define a vector function.
A vector function, denoted as $$\mathbf y = \mathbf f(x)$$ is a function that returns a vector $$\mathbf y$$ . It can have a vector or a scalar argument.

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A vector function, denoted as $$\mathbf y = \mathbf f(x)$$ is a function that returns a vector $$\mathbf y$$ . It can have a vector or a scalar argument.

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Tags
#MLBook #expectation #expected-value #machine-learning #statistics
Question
Describe the expectation of a random variable.

Let a discrete random variable $$X$$ have $$k$$ possible values $$\{ x_i \}_{i=1}^k$$. The expectation of $$X$$ denoted as $$\mathbb E[X]$$ is given by,

\begin{align} \mathbb E[X] & \stackrel{\textrm{def}}{=} \sum_{i=1}^k \left[ x_i \cdot \textrm{Pr} \left( X = x_i \right) \right] \\ & = x_1 \cdot \textrm{Pr} \left( X = x_1 \right) + x_2 \cdot \textrm{Pr} \left( X = x_2 \right) + \cdots + x_k \cdot \textrm{Pr} \left( X = x_k \right) \end{align}

where $$\textrm{Pr} \left( X = x_i \right)$$ is the probability that $$X$$ has the value $$x_i$$ according to the pmf. The expectation of a random variable is also called the mean, average or expected value and is frequently denoted with the letter $$\mu$$ . The expectation is one of the most important statistics of a random variable.

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Let a discrete random variable $$X$$ have $$k$$ possible values $$\{ x_i \}_{i=1}^k$$. The expectation of $$X$$ denoted as $$\mathbb E[X]$$ is given by, $$\mathbb E[X] \stackrel{\textrm{def}}{=} \sum_{i=1}^k \left[ x_i \cdot \textrm{Pr} \left( X = x_i \right) \right] \\ = x_1 \cdot \textrm{Pr} \left( X = x_1 \right) + x_2 \cdot \textrm{Pr} \left( X = x_2 \right) + \cdots + x_k \cdot \textrm{Pr} \left( X = x_k \right)$$ where $$\textrm{Pr} \left( X = x_i \right)$$ is the probability that $$X$$ has the value $$x_i$$ according to the pmf. The expectation of a random variable is also called the mean, average or expected value and is frequently denoted with the letter $$\mu$$ . The expectation is one of the most important statistics of a random variable.

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Tags
#MLBook #has-images #machine-learning #pmf #probability-distribution #probability-mass-function
Question
Describe what is a probability mass function.
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The probability distribution of a discrete random variable is described by a list of probabilities associated with each of its possible values. This list of probabilities is called a probability mass function (pmf). For example: $$\operatorname{Pr}(X=red) = 0.3$$, $$\operatorname{Pr}(X=yellow) = 0.45$$, $$\operatorname{Pr}(X=blue) = 0.25$$. Each probability in a probability mass function is a value greater than or equal to 0. The sum of probabilities equals 1 (Figure 3a).

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The probability distribution of a discrete random variable is described by a list of probabilities associated with each of its possible values. This list of probabilities is called a probability mass function (pmf). For example: $$\operatorname{Pr}(X=red) = 0.3$$, $$\operatorname{Pr}(X=yellow) = 0.45$$, $$\operatorname{Pr}(X=blue) = 0.25$$. Each probability in a probability mass function is a value greater than or equal to 0. The sum of probabilities equals 1 (Figure 3a).

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Tags
#MLBook #holdout-sets #hyperparameters #learning-algorithm-selection #machine-learning #test-set #validation-set
Question
Why do we need two holdout sets and not one?
We use the validation set to 1) choose the learning algorithm and 2) find the best values of hyperparameters. We use the test set to assess the model before delivering it to the client or putting it in production.

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Why do we need two holdout sets and not one? We use the validation set to 1) choose the learning algorithm and 2) find the best values of hyperparameters. We use the test set to assess the model before delivering it to the client or putting it in production.

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#### Annotation 4965970087180

 Softmax is implemented through a neural network layer just before the output layer.

Multi-Class Neural Networks: Softmax | Machine Learning Crash Course
analysis we saw in Figure 1, Softmax might produce the following likelihoods of an image belonging to a particular class: Class Probability apple 0.001 bear 0.04 candy 0.008 dog 0.95 egg 0.001 <span>Softmax is implemented through a neural network layer just before the output layer. The Softmax layer must have the same number of nodes as the output layer. Figure 2. A Softmax layer within a neural network. Click the plus icon to see the Softmax equation. The Softmax

#### Annotation 4965972184332

 The Softmax layer must have the same number of nodes as the output layer.

Multi-Class Neural Networks: Softmax | Machine Learning Crash Course
image belonging to a particular class: Class Probability apple 0.001 bear 0.04 candy 0.008 dog 0.95 egg 0.001 Softmax is implemented through a neural network layer just before the output layer. <span>The Softmax layer must have the same number of nodes as the output layer. Figure 2. A Softmax layer within a neural network. Click the plus icon to see the Softmax equation. The Softmax equation is as follows: p ( y = j | x ) = e ( w j T x + b j ) ∑ k ∈ K e (

#### Annotation 4965973757196

 Softmax assumes that each example is a member of exactly one class. Some examples, however, can simultaneously be a member of multiple classes. For such examples: You may not use Softmax.You must rely on multiple logistic regressions.

Multi-Class Neural Networks: Softmax | Machine Learning Crash Course
is small but becomes prohibitively expensive when the number of classes climbs. Candidate sampling can improve efficiency in problems having a large number of classes. One Label vs. Many Labels <span>Softmax assumes that each example is a member of exactly one class. Some examples, however, can simultaneously be a member of multiple classes. For such examples: You may not use Softmax. You must rely on multiple logistic regressions. For example, suppose your examples are images containing exactly one item—a piece of fruit. Softmax can determine the likelihood of that one item being a pear, an orange, an apple, and

#### Annotation 4965975330060

 [unknown IMAGE 4763872791820] #MLBook #SVM #dataset #decision-boundary #has-images #hyperplane #learning-algorithm #machine-learning #margin #model #support-vector-machine #training Let’s say the problem that you want to solve using supervised learning is spam detection. You gather the data, for example, 10,000 email messages, each with a label either “spam” or “not_spam” (you could add those labels manually or pay someone to do that for us). Now, you have to convert each email message into a feature vector. The data analyst decides, based on their experience, how to convert a real-world entity, such as an email message, into a feature vector. One common way to convert a text into a feature vector, called bag of words, is to take a dictionary of English words (let’s say it contains 20,000 alphabetically sorted words) and stipulate that in our feature vector: the first feature is equal to 1 if the email message contains the word “a”; otherwise, this feature is 0;the second feature is equal to 1 if the email message contains the word “aaron”; otherwise, this feature equals 0;. . .the feature at position 20,000 is equal to 1 if the email message contains the word “zulu”; otherwise, this feature is equal to 0. You repeat the above procedure for every email message in our collection, which gives us 10,000 feature vectors (each vector having the dimensionality of 20,000) and a label (“spam”/“not_spam”). Now you have a machine-readable input data, but the output labels are still in the form of human-readable text. Some learning algorithms require transforming labels into numbers. For example, some algorithms require numbers like 0 (to represent the label “not_spam”) and 1 (to represent the label “spam”). The algorithm I use to illustrate supervised learning is called Support Vector Machine (SVM). This algorithm requires that the positive label (in our case it’s “spam”) has the numeric value of +1 (one), and the negative label (“not_spam”) has the value of −1 (minus one). At this point, you have a dataset and a learning algorithm, so you are ready to apply the learning algorithm to the dataset to get the model. SVM sees every feature vector as a point in a high-dimensional space (in our case, space is 20,000-dimensional). The algorithm puts all feature vectors on an imaginary 20,000-dimensional plot and draws an imaginary 19,999-dimensional line (a hyperplane) that separates examples with positive labels from examples with negative labels. In machine learning, the boundary separating the examples of different classes is called the decision boundary. The equation of the hyperplane is given by two parameters, a real-valued vector $$\mathbf w$$ of the same dimensionality as our input feature vector $$\mathbf x$$, and a real number $$\mathbf b$$ like this: $$\mathbf w \mathbf x − b = 0$$, where the expression $$\mathbf w \mathbf x$$ means $$w^{(1)} x^{(1)} + w^{(2)} x^{(2)} _ \ldots w^{(D)} x^{(D)}$$, and $$D$$ is the number of dimensions of the feature vector $$\mathbf x$$. (If some equations aren’t clear to you right now, in Chapter 2 we revisit the math and statistical concepts necessary to understand them. For the moment, try to get an intuition of what’s happening here. It all becomes more clear after you read the next chapter.) Now, the predicted label for some input feature vector $$\mathbf x$$ is given like this: $$y = \operatorname{sign} \left( \mathbf w \mathbf x − b \right)$$, where sign is a mathematical operator that takes any value as input and returns +1 if the input is a positive number or −1 if the input is a negative number. The goal of the learning algorithm — SVM in this case — is to leverage the dataset and find the optimal...

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#### Annotation 4965985815820

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the Apple Command key function from my Windows keyboard. I would think that the Windows key would work but it doesn't. Is there a way to map the Windows key to the Apple Command key? I really w<span>ant to be able to invoke copy and paste from the keyboard, which are Command-C and Command-V on the iMac, so I'm stuck because I don't have a "Command" key. macos keyboard-shortcuts chrome-remote-desktop share Share a link to this question Copy link |improve this question edit

#### Flashcard 4966128422156

Tags
#MLBook #SVM #dataset #decision-boundary #has-images #hyperplane #learning-algorithm #machine-learning #margin #model #support-vector-machine #training
Question
Describe how Support Vector Machines work by using a linear model as an example.
[unknown IMAGE 4763872791820]

Let’s say the problem that you want to solve using supervised learning is spam detection. You gather the data, for example, 10,000 email messages, each with a label either “spam” or “not_spam” (you could add those labels manually or pay someone to do that for us). Now, you have to convert each email message into a feature vector.

The data analyst decides, based on their experience, how to convert a real-world entity, such as an email message, into a feature vector. One common way to convert a text into a feature vector, called bag of words, is to take a dictionary of English words (let’s say it contains 20,000 alphabetically sorted words) and stipulate that in our feature vector:

• the first feature is equal to 1 if the email message contains the word “a”; otherwise, this feature is 0;
• the second feature is equal to 1 if the email message contains the word “aaron”; otherwise, this feature equals 0;
• . . .
• the feature at position 20,000 is equal to 1 if the email message contains the word “zulu”; otherwise, this feature is equal to 0.

You repeat the above procedure for every email message in our collection, which gives us 10,000 feature vectors (each vector having the dimensionality of 20,000) and a label (“spam”/“not_spam”).

Now you have a machine-readable input data, but the output labels are still in the form of human-readable text. Some learning algorithms require transforming labels into numbers. For example, some algorithms require numbers like 0 (to represent the label “not_spam”) and 1 (to represent the label “spam”). The algorithm I use to illustrate supervised learning is called Support Vector Machine (SVM). This algorithm requires that the positive label (in our case it’s “spam”) has the numeric value of +1 (one), and the negative label (“not_spam”) has the value of −1 (minus one).

At this point, you have a dataset and a learning algorithm, so you are ready to apply the learning algorithm to the dataset to get the model.

SVM sees every feature vector as a point in a high-dimensional space (in our case, space is 20,000-dimensional). The algorithm puts all feature vectors on an imaginary 20,000-dimensional plot and draws an imaginary 19,999-dimensional line (a hyperplane) that separates examples with positive labels from examples with negative labels. In machine learning, the boundary separating the examples of different classes is called the decision boundary.

The equation of the hyperplane is given by two parameters, a real-valued vector $$\mathbf w$$ of the same dimensionality as our input feature vector $$\mathbf x$$, and a real number $$\mathbf b$$ like this:

$$\mathbf w \mathbf x − b = 0$$,

where the expression $$\mathbf w \mathbf x$$ means $$w^{(1)} x^{(1)} + w^{(2)} x^{(2)} _ \ldots +w^{(D)} x^{(D)}$$, and $$D$$ is the number of dimensions of the feature vector $$\mathbf x$$.

(If some equations aren’t clear to you right now, in Chapter 2 we revisit the math and statistical concepts necessary to understand them. For the moment, try to get an intuition of what’s happening here. It all becomes more clear after you read the next chapter.)

Now, the predicted label for some input feature vector $$\mathbf x$$ is given like this: $$y = \operatorname{sign} \left( \mathbf w \mathbf x − b \right)$$, where sign is a mathematical operator that takes any value as input and returns +1 if the input is a positive number or −1 if the input is a negative number. The goal of the learning algorithm — SVM in this case — is to leverage the dataset and find the optima

...

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Let’s say the problem that you want to solve using supervised learning is spam detection. You gather the data, for example, 10,000 email messages, each with a label either “spam” or “not_spam” (you could add those labels manually or pay someone to do that for us). Now, you have to convert each email message into a feature vector. The data analyst decides, based on their experience, how to convert a real-world entity, such as an email message, into a feature vector. One common way to convert a text into a feature vector, called bag of words, is to take a dictionary of English words (let’s say it contains 20,000 alphabetically sorted words) and stipulate that in our feature vector: the first feature is equal to 1 if the email message contains the word “a”; otherwise, this feature is 0; the second feature is equal to 1 if the email message contains the word “aaron”; otherwise, this feature equals 0; . . . the feature at position 20,000 is equal to 1 if the email message contains the word “zulu”; otherwise, this feature is equal to 0. You repeat the above procedure for every email message in our collection, which gives us 10,000 feature vectors (each vector having the dimensionality of 20,000) and a label (“spam”/“not_spam”). Now you have a machine-readable input data, but the output labels are still in the form of human-readable text. Some learning algorithms require transforming labels into numbers. For example, some algorithms require numbers like 0 (to represent the label “not_spam”) and 1 (to represent the label “spam”). The algorithm I use to illustrate supervised learning is called Support Vector Machine (SVM). This algorithm requires that the positive label (in our case it’s “spam”) has the numeric value of +1 (one), and the negative label (“not_spam”) has the value of −1 (minus one). At this point, you have a dataset and a learning algorithm, so you are ready to apply the learning algorithm to the dataset to get the model. SVM sees every feature vector as a point in a high-dimensional space (in our case, space is 20,000-dimensional). The algorithm puts all feature vectors on an imaginary 20,000-dimensional plot and draws an imaginary 19,999-dimensional line (a hyperplane) that separates examples with positive labels from examples with negative labels. In machine learning, the boundary separating the examples of different classes is called the decision boundary. The equation of the hyperplane is given by two parameters, a real-valued vector $$\mathbf w$$ of the same dimensionality as our input feature vector $$\mathbf x$$, and a real number $$\mathbf b$$ like this: $$\mathbf w \mathbf x − b = 0$$, where the expression $$\mathbf w \mathbf x$$ means $$w^{(1)} x^{(1)} + w^{(2)} x^{(2)} _ \ldots w^{(D)} x^{(D)}$$, and $$D$$ is the number of dimensions of the feature vector $$\mathbf x$$. (If some equations aren’t clear to you right now, in Chapter 2 we revisit the math and statistical concepts necessary to understand them. For the moment, try to get an intuition of what’s happening here. It all becomes more clear after you read the next chapter.) Now, the predicted label for some input feature vector $$\mathbf x$$ is given like this: $$y = \operatorname{sign} \left( \mathbf w \mathbf x − b \right)$$, where sign is a mathematical operator that takes any value as input and returns +1 if the input is a positive number or −1 if the input is a negative number. The goal of the learning algorithm — SVM in this case — is to leverage the dataset and find the optimal values $$\mathbf w^\ast$$ and $$b^\ast$$ for parameters $$\mathbf w$$ and $$b$$ . Once the learning algorithm identifies these optimal values, the model $$f(x)$$ is then defined as: $$f(x) = \operatorname{sign} \left( \mathbf w^\ast \mathbf x − b^\ast \right)$$ Therefore, to predict whether an email message is spam or not spam using an SVM model, you have to take a text of the message, convert it into a feature vector, then multiply this vector by $$\mathbf w^\ast$$, subtract $$b^\ast$$ and take the sign of the result. This will give us the prediction (+1 means “spam”, −1 means “not_spam”). Now, how does the machine find $$\mathbf w^\ast$$ and $$b^\ast$$? It solves an optimization problem. Machines are good at optimizing functions under constraints. So what are the constraints we want to satisfy here? First of all, we want the model to predict the labels of our 10,000 examples correctly. Remember that each example $$i = 1 ,\ldots, 10000$$ is given by a pair $$\left( \mathbf x_i, y_i \right)$$, where $$\mathbf x_i$$ is the feature vector of example $$i$$ and $$y_i$$ is its label that takes values either −1 or +1. So the constraints are naturally: \begin{align} \mathbf w \mathbf x_i − b & \ge +1, \quad \textrm{if} \; y_i = +1, \\ \mathbf w \mathbf x_i − b & \le -1, \quad \textrm{if} \; y_i = -1. \end{align} We would also prefer that the hyperplane separates positive examples from negative ones with the largest margin. The margin is the distance between the closest examples of two classes, as defined by the decision boundary. A large margin contributes to a better generalization, that is how well the model will classify new examples in the future. To achieve that, we need to minimize the Euclidean norm of $$\mathbf w$$ denoted by $$\Vert \mathbf w \Vert$$ and given by $$\sqrt{\sum_{j=1}^D \left( w^{(j)}\right)^2}$$. So, the optimization problem that we want the machine to solve looks like this: Minimize $$\Vert \mathbf w \Vert$$ subject to $$y_i \left( \mathbf w \mathbf x_i − b \right) \ge 1 \; \textrm{for} \; i = 1 , \ldots , N$$ . The expression $$y_i \left( \mathbf w \mathbf x_i − b \right) \ge 1$$ is just a compact way to write the above two constraints. The solution of this optimization problem, given by $$\mathbf w^\ast$$ and $$b^\ast$$, is called the statistical model, or, simply, the model. The process of building the model is called training. For two-dimensional feature vectors, the problem and the solution can be visualized as shown in Figure 1. The blue and orange circles represent, respectively, positive and negative examples, and the line given by $$\mathbf w \mathbf x − b = 0$$ is the decision boundary. Why, by minimizing the norm of $$\mathbf w$$, do we find the highest margin between the two classes? Geometrically, the equations $$\mathbf w \mathbf x − b = 1$$ and $$\mathbf w \mathbf x − b = -1$$ define two parallel hyperplanes, as you see in Figure 1. The distance between these hyperplanes is given by $$2/\Vert \mathbf w \Vert$$ , so the smaller the norm $$\Vert \mathbf w \Vert$$, the larger the distance between these two hyperplanes. That’s how Support Vector Machines work. This particular version of the algorithm builds the so-called linear model. It’s called linear because the decision boundary is a straight line (or a plane, or a hyperplane).

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