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Flashcard 7791737965836

Tags
#deep-learning #keras #lstm #python #sequence
Question
LSTM’s special strength, namely, to learn to remember single events for [...], unknown time periods
Answer
very long

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LSTM’s special strength, namely, to learn to remember single events for very long, unknown time periods

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Flashcard 7791740849420

Tags
#deep-learning #keras #lstm #python #sequence
Question
Each [...] can be partitioned into groups of input-output pattern pairs called batches
Answer
epoch

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Each epoch can be partitioned into groups of input-output pattern pairs called batches

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Flashcard 7791745568012

Tags
#data-science #infrastructure
Question
incidental complexity = complexity that is not necessitated by the problem itself but is an unwanted artifact of a [...]
Answer
chosen approach

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incidental complexity = complexity that is not necessitated by the problem itself but is an unwanted artifact of a chosen approach

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Flashcard 7791748451596

Tags
#tensorflow #tensorflow-certificate
Question

# Create confusion metrics

from [...].metrics import confusion_matrix

Answer
sklearn

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# Create confusion metrics from sklearn.metrics import confusion_matrix

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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 7791750024460

Tags
#tensorflow #tensorflow-certificate
Question

# Create confusion metrics

from sklearn.metrics import [...]

Answer
confusion_matrix

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# Create confusion metrics from sklearn.metrics import confusion_matrix

Original toplevel document

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