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Annotation 7674170313996

English: sentence mining

#English #sentence_mining #vocabulary

This course prepares students for middle and senior managerial positions.

status	not read	reprioritisations
last reprioritisation on		suggested re-reading day
started reading on		finished reading on

Flashcard 7674172149004

Tags

#English #sentence_mining #vocabulary

Question

This course prepares students [...] middle and senior managerial positions.

Answer

for

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English: sentence mining
This course prepares students for middle and senior managerial positions.

Flashcard 7674173984012

Tags

#tensorflow #tensorflow-certificate

Question

Confusion matrix

y-axis -> [...] label

Answer

true

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Confusion matrix y-axis -> true label

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TfC_02_classification-PART_2
leads to less false negatives. Tradeoff between recall and precision. F1-score Combination of precision and recall, ususally a good overall metric for classification models. keyboard_arrow_down <span>Confusion matrix Can be hard to use whith large numbers of classes. y-axis -> true label x-axis -> predicted label # Create confusion metrics from sklearn.metrics import confusion_matrix y_preds = model_8.predict(X_test) confusion_matrix(y_test, y_preds) important: This time there is a problem with loss function. In case of categorical_crossentropy the labels have to be one-hot encoded In case of labels as integeres use SparseCategorica

Flashcard 7674175819020

Tags

#recurrent-neural-networks #rnn

Question

More precisely, firms following a customer-centric business approach need to know how their clientele will behave on [...] future time scales and levels of behavioral complexity (Gupta & Lehmann, 2005; Fader, 2020):

- What are they going to do in the immediate future and when do they make their next transaction with the focal company, if any?

- Are some of them at risk of stopping doing business with the firm?

- How exactly do seasonality and other time-based events influence the propensity of customers to buy?

Answer

different

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More precisely, firms following a customer-centric business approach need to know how their clientele will behave on different future time scales and levels of behavioral complexity (Gupta & Lehmann, 2005; Fader, 2020): - What are they going to do in the immediate future and when do they make their next tra

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Flashcard 7674178440460

Tags

#deep-learning #has-images #keras #lstm #python #sequence

[unknown IMAGE 7104071601420]

Question

Keras provides flexibility to [...] the resetting of internal state from updates to network weights by defining an LSTM layer as stateful. This can be done by setting the stateful argument on the LSTM layer to True

Answer

decouple

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Keras provides flexibility to decouple the resetting of internal state from updates to network weights by defining an LSTM layer as stateful. This can be done by setting the stateful argument on the LSTM layer to True

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Flashcard 7674180275468

Tags

#DAG #causal #inference

Question

we focus on the [...] and estimation of causal eﬀects in populations, that is, numerical quantities that measure changes in the distribution of an outcome under diﬀerent interventions.

Answer

identification

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we focus on the identification and estimation of causal eﬀects in populations, that is, numerical quantities that measure changes in the distribution of an outcome under diﬀerent interventions.

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Flashcard 7674182372620

Tags

#tensorflow #tensorflow-certificate

Question

[...] is a common batch size

Answer

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32 is a common batch size

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TfC_02_classification-PART_1
ssification - a sample can be assigned to more than one label from more than 2 label options Multiclass classification - a sample can be assigned to one label but from more than 2 label options <span>Multiclass image classificaton: pizza, steak, sushi Input_shape = [None, 224, 224, 3] - single image Input shape = [32, 224, 224, 3] - common batch size of images 32 is a common batch size How to generate such data? from sklearn.datasets import make_circles # Make 1000 examples n_samples=1000 # Create circles X, y = make_circles(n_samples, noise=0.03, random_state=42) How

Flashcard 7674184207628

Tags

#tensorflow #tensorflow-certificate

Question

F1-score

Combination of precision and recall, ususally a good [...] metric for classification models.

Answer

overall

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F1-score Combination of precision and recall, ususally a good overall metric for classification models.

Original toplevel document

TfC_02_classification-PART_2
anced classes Precision For imbalanced class problems. Higher precision leads to less false positives. Recall Higher recall leads to less false negatives. Tradeoff between recall and precision. <span>F1-score Combination of precision and recall, ususally a good overall metric for classification models. keyboard_arrow_down Confusion matrix Can be hard to use whith large numbers of classes. y-axis -> true label x-axis -> predicted label # Create confusion metrics from sklearn.metr

Flashcard 7674186042636

Tags

#DAG #causal #edx

Question

For example, suppose L is fetal death. We don't know the [...] causal DAG, we propose seven causal DAGs. Suppose that L does not help block a backdoor path in any of the seven DAGs, then we will not adjust for L, even if L were strongly associated with A and Y.

Answer

true

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For example, suppose L is fetal death. We don't know the true causal DAG, we propose seven causal DAGs. Suppose that L does not help block a backdoor path in any of the seven DAGs, then we will not adjust for L, even if L were strongly associated

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Flashcard 7674188139788

Tags

#abm #agent-based #has-images #machine-learning #model #priority

[unknown IMAGE 7096340712716]

Question

During the Experience phase agents make random decisions and add new entries to the database consisting of [...] with all their sensory input, the randomly chosen action and the result, i.e. if the score increased, decreased or stayed the same due to this decision.

Answer

a vector

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During the Experience phase agents make random decisions and add new entries to the database consisting of a vector with all their sensory input, the randomly chosen action and the result, i.e. if the score increased, decreased or stayed the same due to this decision.

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Flashcard 7674212257036

Tags

#Maladies-infectieuses-et-tropicales #Mumps #Mumps-MDB #Oreillons #Oreillons-MDB #Virologie #Virology

Question

Oreillons

The genome codes for eight proteins—the [...] protein (HN), [etc...]

Answer

Hemagglutinin-Neuraminidase

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The genome codes for eight proteins—the hemagglutinin-neuraminidase protein (HN), fusion protein (F), nucleocapsid protein (NP), phosphoprotein (P), matrix protein (M), hydrophobic protein (SH), and L proteins.7

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Flashcard 7674214616332

Tags

#Maladies-infectieuses-et-tropicales #Mumps #Mumps-MDB #Oreillons #Oreillons-MDB #Virologie #Virology

Question

Oreillons

The genome codes for eight proteins—the [...] protein (F), [etc...]

Answer

Fusion

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Flashcard 7674217237772

Tags

#MMR #Maladies-infectieuses-et-tropicales #Measles #MedicalMicrobiology2013-MMR #MedicalMicrobiology2013-Paramyxorividae #MedicalMicrobiology2013-Paramyxovirus #MedicalMicrobiology2013-ROR #Mumps #Oreillons #Paramyxorividae #Paramyxovirus #ROR #Rougeole #Rubella #Rubeole #Virologie #Virology

Question

Quels sont les deux types fonctionnels de glycoprotéines exprimés à la surface de l'enveloppe du virus des oreillons ?

Answer

Glycoprotéine de Fusion
Glycoprotéine d'Adhésion

"The envelope contains two glycoproteins, a fusion (F) protein, and a viral attachment protein (hemagglutinin-neuraminidase [HN], hemagglutinin [H], or glycoprotein [G] protein)"

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The envelope con- tains two glycoproteins, a fusion (F) protein, and a viral attachment protein (hemagglutinin-neuraminidase [HN], hemagglutinin [H], or glycoprotein [G] protein)

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Edited, memorised or added to reading queue

on 08-Jan-2025 (Wed)

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