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Flashcard 7629349981452

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#feature-engineering #has-images #lstm #recurrent-neural-networks #rnn
[unknown IMAGE 7103902780684]
Question
Fig. 2. Classic feedforward neural network (A), [...] neural network (B), and “unrolled” graphical representation of a recurrent neural network (C) where we use sequence data (x 1 ,x 2 ,x 3 ) to make sequence predictions (y 1 ,y 2 ,y 3 ) while preserving information through the hidden states h 1 ,h 2 ,h 3
Answer
recurrent

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Fig. 2. Classic feedforward neural network (A), recurrent neural network (B), and “unrolled” graphical representation of a recurrent neural network (C) where we use sequence data (x 1 ,x 2 ,x 3 ) to make sequence predictions (y 1 ,y 2 ,y 3 ) w

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Flashcard 7629359942924

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#Docker
Question

Docker look at the log of an exited container (with [...])

docker logs -t [NAZWA KONTENERA]
Answer
timestamps

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Docker look at the log of an exited container (with timestamps) docker logs -t [NAZWA KONTENERA]







Flashcard 7629443829004

Tags
#deep-learning #keras #lstm #python #sequence
Question
How to Convert Categorical Data to Numerical Data This involves two steps: 1. [...] Encoding. 2. One Hot Encoding.
Answer
Integer

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How to Convert Categorical Data to Numerical Data This involves two steps: 1. Integer Encoding. 2. One Hot Encoding.

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Flashcard 7629446974732

Tags
#feature-engineering #lstm #recurrent-neural-networks #rnn
Question
Because of their typical high-dimensionality, the [...] of RNN models are usually more potent than that of hidden markov models (e.g., Netzer, Lattin, & Srinivasan, 2008), which are commonly used in marketing to capture customer dynamics.
Answer
hidden states

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Because of their typical high-dimensionality, the hidden states of RNN models are usually more potent than that of hidden markov models (e.g., Netzer, Lattin, & Srinivasan, 2008), which are commonly used in marketing to capture customer dynamics

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Flashcard 7629495209228

Tags
#advanced #deep-learning #keras #python
Question

# Import the sigmoid function from scipy

from scipy.special import expit as [...]

# Weight from the model

weight = 0.14

# Print the approximate win probability predicted close game

print(sigmoid(1 * 0.14))

# Print the approximate win probability predicted blowout game

print(sigmoid(10 * 0.14))

Answer
sigmoid

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# Import the sigmoid function from scipy from scipy.special import expit as sigmoid # Weight from the model weight = 0.14 # Print the approximate win probability predicted close game print(sigmoid(1 * 0.14)) # Print the approximate win probability predicted blowout gam







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Coronaviruses are enveloped positive-sensed, single-stranded RNA viruses.
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and diagnosis".) See specific topic reviews for details on complications of СOVІD-19 and issues related to СOVΙD-19 in other patient populations. VIROLOGY AND IMMUNOLOGY Coronavirus virology — <span>Coronaviruses are enveloped positive-sensed, single-stranded RNA viruses. ЅARЅ-СoV-2 is a betacoronavirus. It is in the same subgenus as, but is in a different clade from, the severe acute respiratory syndrome (ЅARЅ) virus. The Middle East Respiratory Syndrom




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ЅARЅ-СoV-2 is a betacoronavirus. It is in the same subgenus as, but is in a different clade from, the severe acute respiratory syndrome (ЅARЅ) virus
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of СOVІD-19 and issues related to СOVΙD-19 in other patient populations. VIROLOGY AND IMMUNOLOGY Coronavirus virology — Coronaviruses are enveloped positive-sensed, single-stranded RNA viruses. <span>ЅARЅ-СoV-2 is a betacoronavirus. It is in the same subgenus as, but is in a different clade from, the severe acute respiratory syndrome (ЅARЅ) virus. The Middle East Respiratory Syndrome (MERS) virus is a more distantly related betacoronavirus [7,8]. The closest RNA sequence similarities to ЅARЅ-CoV-2 are found in coronaviruses isol




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The Middle East Respiratory Syndrome (MERS) virus is a more distantly related betacoronavirus [ 7,8].
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sitive-sensed, single-stranded RNA viruses. ЅARЅ-СoV-2 is a betacoronavirus. It is in the same subgenus as, but is in a different clade from, the severe acute respiratory syndrome (ЅARЅ) virus. <span>The Middle East Respiratory Syndrome (MERS) virus is a more distantly related betacoronavirus [7,8]. The closest RNA sequence similarities to ЅARЅ-CoV-2 are found in coronaviruses isolated from bats, which were the likely animal reservoir prior to spillover to humans [9,10]. Pangolins




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The closest RNA sequence similarities to ЅARЅ-CoV-2 are found in coronaviruses isolated from bats, which were the likely animal reservoir prior to spillover to humans [ 9,10]. Pangolins are a possible intermediate host for ЅΑRЅ-СoV-2 prior to passage to humans.
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us as, but is in a different clade from, the severe acute respiratory syndrome (ЅARЅ) virus. The Middle East Respiratory Syndrome (MERS) virus is a more distantly related betacoronavirus [7,8]. <span>The closest RNA sequence similarities to ЅARЅ-CoV-2 are found in coronaviruses isolated from bats, which were the likely animal reservoir prior to spillover to humans [9,10]. Pangolins are a possible intermediate host for ЅΑRЅ-СoV-2 prior to passage to humans. The virology of coronaviruses is discussed in detail elsewhere. (See "Common cold coronaviruses", section on 'Coronavirus virology'.) Structure and function of the SARS-CoV-2 spike prot




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The primary host cell entry receptor for ЅΑRЅ-CоV-2 is the same as for SARS-CoV, the angiotensin-converting enzyme 2 (ACE2) [11]. ЅΑRS-СоV-2 binds to ACE2 through the receptor-binding domain of its spike protein (figure 1).
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detail elsewhere. (See "Common cold coronaviruses", section on 'Coronavirus virology'.) Structure and function of the SARS-CoV-2 spike protein Figure 1 SARS-CoV-2 cell entry and key proteins — <span>The primary host cell entry receptor for ЅΑRЅ-CоV-2 is the same as for SARS-CoV, the angiotensin-converting enzyme 2 (ACE2) [11]. ЅΑRS-СоV-2 binds to ACE2 through the receptor-binding domain of its spike protein (figure 1). Additional enzymatic interactions between ЅΑRS-CoV-2 and host cells determine specific cell tropism, which has evolved with the virus. Specifically, following ACE2 binding, the original




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Additional enzymatic interactions between ЅΑRS-CoV-2 and host cells determine specific cell tropism, which has evolved with the virus. Specifically, following ACE2 binding, the original ЅARЅ-СoV-2 virus and some subsequent variants, including Delta, enter cells after a host protease (most often the TMPRSS2 enzyme) cleaves the spike protein, enabling subsequent membrane fusion and cell entry [12,13]. Since TMPRSS2 is expressed at high levels in lung epithelial cells, these variants replicated efficiently in the lungs. Omicron-lineage variants, in contrast, mostly enter host cells through a different pathway involving cathepsin-dependent endocytosis and use the TMPRSS2-mediated pathway far less efficiently [14]. Thus, Omicron-lineage variants replicate more efficiently in the upper airways, where TMPRSS2 expression is lower than in the lungs [15].
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ceptor for ЅΑRЅ-CоV-2 is the same as for SARS-CoV, the angiotensin-converting enzyme 2 (ACE2) [11]. ЅΑRS-СоV-2 binds to ACE2 through the receptor-binding domain of its spike protein (figure 1). <span>Additional enzymatic interactions between ЅΑRS-CoV-2 and host cells determine specific cell tropism, which has evolved with the virus. Specifically, following ACE2 binding, the original ЅARЅ-СoV-2 virus and some subsequent variants, including Delta, enter cells after a host protease (most often the TMPRSS2 enzyme) cleaves the spike protein, enabling subsequent membrane fusion and cell entry [12,13]. Since TMPRSS2 is expressed at high levels in lung epithelial cells, these variants replicated efficiently in the lungs. Omicron-lineage variants, in contrast, mostly enter host cells through a different pathway involving cathepsin-dependent endocytosis and use the TMPRSS2-mediated pathway far less efficiently [14]. Thus, Omicron-lineage variants replicate more efficiently in the upper airways, where TMPRSS2 expression is lower than in the lungs [15]. (See 'Viral evolution and variants of concern' below.) ЅΑRS-CoV-2 antivirals have targeted one of two important viral proteins: ●RNA-dependent RNA polymerase (RdRp) – This is an error-p




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ЅΑRS-CoV-2 antivirals have targeted one of two important viral proteins:

● RNA-dependent RNA polymerase (RdRp) – This is an error-prone enzyme responsible for viral replication [16]. Remdesivir, molnupiravir, and mindeudesivir (a drug available in Сhiոa) target the RdRp.

● 3CL protease – This is also called the main protease, and it cleaves the polyproteins encoded by the ЅΑRЅ-СоV-2 RNA. Ritonavir-boosted nirmatrelvir [17], ritonavir-boosted simnotrelvir, and ensitrelvir target this protease.

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Thus, Omicron-lineage variants replicate more efficiently in the upper airways, where TMPRSS2 expression is lower than in the lungs [15]. (See 'Viral evolution and variants of concern' below.) <span>ЅΑRS-CoV-2 antivirals have targeted one of two important viral proteins: ●RNA-dependent RNA polymerase (RdRp) – This is an error-prone enzyme responsible for viral replication [16]. Remdesivir, molnupiravir, and mindeudesivir (a drug available in Сhiոa) target the RdRp. ●3CL protease – This is also called the main protease, and it cleaves the polyproteins encoded by the ЅΑRЅ-СоV-2 RNA. Ritonavir-boosted nirmatrelvir [17], ritonavir-boosted simnotrelvir, and ensitrelvir target this protease. Viral evolution and variants of concern SARS-CoV-2 evolution and nomenclature ●Mechanisms of evolution – ЅΑRS-CoV-2, like many other RNA viruses, has evolved over time through random mu




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ЅΑRS-CoV-2, like many other RNA viruses, has evolved over time through random mutations from replication infidelity, although its intrinsic mutation rate is lower than many other RNA viruses (including HIV and HCV) because of a proofreading mechanism [18].
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nirmatrelvir [17], ritonavir-boosted simnotrelvir, and ensitrelvir target this protease. Viral evolution and variants of concern SARS-CoV-2 evolution and nomenclature ●Mechanisms of evolution – <span>ЅΑRS-CoV-2, like many other RNA viruses, has evolved over time through random mutations from replication infidelity, although its intrinsic mutation rate is lower than many other RNA viruses (including HIV and HCV) because of a proofreading mechanism [18]. Recombination is another mechanism for evolution. This occurs when a host is simultaneously infected with two distinct ЅARЅ-СоV-2 viruses, and a progeny virus emerges that includes RNA




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Recombination is another mechanism for evolution. This occurs when a host is simultaneously infected with two distinct ЅARЅ-СоV-2 viruses, and a progeny virus emerges that includes RNA sequences from both [19].
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through random mutations from replication infidelity, although its intrinsic mutation rate is lower than many other RNA viruses (including HIV and HCV) because of a proofreading mechanism [18]. <span>Recombination is another mechanism for evolution. This occurs when a host is simultaneously infected with two distinct ЅARЅ-СоV-2 viruses, and a progeny virus emerges that includes RNA sequences from both [19]. Rapid accumulation of many mutations may occur in highly immunocompromised individuals with persistent or chronic ЅARЅ-СoV-2 infections [18,20,21]. (See 'Viral shedding and period of in




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Rapid accumulation of many mutations may occur in highly immunocompromised individuals with persistent or chronic ЅARЅ-СoV-2 infections [18,20,21]. (See 'Viral shedding and period of infectiousness' below.)
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another mechanism for evolution. This occurs when a host is simultaneously infected with two distinct ЅARЅ-СоV-2 viruses, and a progeny virus emerges that includes RNA sequences from both [19]. <span>Rapid accumulation of many mutations may occur in highly immunocompromised individuals with persistent or chronic ЅARЅ-СoV-2 infections [18,20,21]. (See 'Viral shedding and period of infectiousness' below.) Detailed study of the various ЅΑRЅ-CоV-2 viruses and variants of concern has illustrated important features of the nature of viral evolution as well as the impact of different viruses o




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Most importantly, selective advantage to a progeny virus mainly comes from intrinsic viral factors (often leading to replication advantage) or from greater evasion of the host immune response (most commonly from antigenic shift)
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iled study of the various ЅΑRЅ-CоV-2 viruses and variants of concern has illustrated important features of the nature of viral evolution as well as the impact of different viruses on the hosts. <span>Most importantly, selective advantage to a progeny virus mainly comes from intrinsic viral factors (often leading to replication advantage) or from greater evasion of the host immune response (most commonly from antigenic shift). ●Variant nomenclature – Although most mutations in the ЅARЅ-CoV-2 genome have no impact on viral function, some convey a selective advantage to the progeny virus, which thus accumulate




Flashcard 7669276347660

Tags
#ML-engineering #ML_in_Action #learning #machine #software-engineering
Question
No one thinks that [...] matters until it’s 4 a.m. on a Saturday, you’re 18 hours into debugging a failure, and you still haven’t fixed the bug
Answer
code quality

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No one thinks that code quality matters until it’s 4 a.m. on a Saturday, you’re 18 hours into debugging a failure, and you still haven’t fixed the bug

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#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data

To summarize, our contributions are the following:

(i) ...

(ii) we provide an empirical comparison of prediction performance on real-world e-commerce data;

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To summarize, our contributions are the following: (i) we show how consumer behavior can be predicted without sophisticated feature engineering by using RNNs; (ii) we provide an empirical comparison of prediction performance on real-world e-commerce data; and (iii) we demonstrate how RNNs are helpful in explaining the predictions for individual consumers

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Flashcard 7669281328396

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#deep-learning #keras #lstm #python #sequence
Question

One Hot Encoding

For categorical variables where [...] ordinal relationship exists, the integer encoding is not enough.

Answer
no

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One Hot Encoding For categorical variables where no such ordinal relationship exists, the integer encoding is not enough.

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Flashcard 7669283687692

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#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question
We are employing RNNs in production now which offers significant advantages over existing methods: reduced feature engineering; improved empirical performance; and better prediction [...]
Answer
explanations

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span> We are employing RNNs in production now which offers significant advantages over existing methods: reduced feature engineering; improved empirical performance; and better prediction explanations <span>

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Flashcard 7669285784844

Tags
#English #vocabulary
Question

innocuous

adjective

UK /ɪˈnɒkjuəs/ US

[...] to upset or harm anyone

Answer
not likely

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innocuous adjective UK /ɪˈnɒkjuəs/ US not likely to upset or harm anyone







Install all the packages that you need in the new environment at the same time. Installing packages one at a time can lead to dependency conflicts.
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Recommendations for Avoiding Dependency Conflicts with Conda There are two simple rules to follow: Always create a new environment for each project Install all the packages that you need in the new environment at the same time. Installing packages one at a time can lead to dependency conflicts. To create an environment with a specific version of Python and multiple packages including a package with a specific version: $ conda create -n <env_name> python=<version#>

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How to Manage Python Dependencies with Conda - ActiveState
rmine the Current Environment with Conda The current or active environment is shown in parentheses () or brackets [] at the beginning of the Anaconda Prompt or terminal: (<current_env>) $ <span>Recommendations for Avoiding Dependency Conflicts with Conda There are two simple rules to follow: Always create a new environment for each project Install all the packages that you need in the new environment at the same time. Installing packages one at a time can lead to dependency conflicts. To create an environment with a specific version of Python and multiple packages including a package with a specific version: $ conda create -n <env_name> python=<version#> <packagename> <packagename> <packagename>=<version#> Alternatively, you can use conda to install all the packages in a requirements.txt file. You can save a requirements.txt file from an existing environment, or manually create a new requirements.txt for a different environment. To create a conda requirements.txt file from an existing environment: Activate your project environment. See section above entitled “How to Activate an Environment with Conda” for detai




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Data scientists are also expected to be familiar with additional realms of competency. From mid-level DE skills (you have to get your data for your data science from somewhere, right?), software development skills, project management skills, visualization skills, and presentation skills
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Data scientists are also expected to be familiar with additional realms of competency. From mid-level DE skills (you have to get your data for your data science from somewhere, right?), software development skills, project management skills, visualization skills, and presentation skills, the list grows ever longer, and the volumes of experience that need to be gained become rather daunting. It’s not much of a surprise, considering all of this, that “just figuring it ou

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[unknown IMAGE 7096178707724] #DAG #causal #edx #has-images #inference
Women who did develop cancer are much more likely to be included in the study than women who did not develop cancer. Therefore, our causal graph will include a note for selection-- C-- an arrow from the outcome Y to C, and a box around C to indicate that the analysis is conditional on having been selected into the study, which means that we are only one arrow away from selection bias.
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As you may have already noticed, the case-control design selects individuals based on their outcome. Women who did develop cancer are much more likely to be included in the study than women who did not develop cancer. Therefore, our causal graph will include a note for selection-- C-- an arrow from the outcome Y to C, and a box around C to indicate that the analysis is conditional on having been selected into the study, which means that we are only one arrow away from selection bias.

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