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on 09-Dec-2025 (Tue)

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Flashcard 7775822679308

Tags
#deep-learning #keras #lstm #python #sequence
Question
Increasing the [...] of the network provides an alternate solution that requires fewer neurons and trains faster
Answer
depth

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Increasing the depth of the network provides an alternate solution that requires fewer neurons and trains faster

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Flashcard 7775826087180

Tags
#tensorflow #tensorflow-certificate
Question

[...] function:

  • In case of categorical_crossentropy the labels have to be one-hot encoded

Answer
loss

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loss function: In case of categorical_crossentropy the labels have to be one-hot encoded

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TfC_02_classification-PART_2
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) <span>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 SparseCategoricalCrossentropy # Get the patterns of a layer in our network weights, biases = model_35.layers[1].get_weights() <span>






Flashcard 7775827660044

Tags
#deep-learning #keras #lstm #python #sequence
Question
Long Short-Term Memory (LSTM) is an RNN architecture specifically designed to address the [...] problem
Answer
vanishing gradient

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Long Short-Term Memory (LSTM) is an RNN architecture specifically designed to address the vanishing gradient problem

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Flashcard 7775829495052

Tags
#recurrent-neural-networks #rnn
Question
The name, often shortened to [...], comes from the fact that these models can translate a sequence of input elements into a sequence of outputs.
Answer
seq2seq

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The name, often shortened to seq2seq, comes from the fact that these models can translate a sequence of input elements into a sequence of outputs.

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