deep-learning-with-python-notebooks
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fchollet
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
PLS HELP ME TO SORT THIS THING OUT   
When trying to run the code for preparing Glove word-embeddings matrix, towards the end of the notebook for chapter 11, part 2, I get an error: embedding_dim = 100 #...
**Description:** Reading the section 10 _Deep learning for timeseries_, there appears to be a potential discrepancy between the problem statement and the actual implementation. **Problem Statement:** The problem statement, as...
I tried to run this file https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/chapter11_part02_sequence-models.ipynb and this section as the below: import tensorflow as tf inputs = keras.Input(shape=(None,), dtype="int64") embedded = tf.one_hot(inputs, depth=max_tokens) x = layers.Bidirectional(layers.LSTM(32))(embedded) x =...
Hi @fchollet, Something came back to me, regarding these notebooks: `chapter11_part04_sequence-to-sequence-learning.ipynb` never defines or imports `TransformerEncoder`, as it comes a bit after the passage that is the basis for `chapter11_part03_transformer.ipynb`,...
I have added the model parameter to generate the text as expected by the TextGenerator class. Earlier the self.model(tokenized_sentence) would not recognize the model. If the model is not trained...
Hi there, First off, congratulations on the new edition of Deep Learning With Python, it's good to see the material refreshed! A quick question came up (I'm updating course material...
Under the section "Sequence models": typo: TextVecotorization correct: TextVectorization
Instead of print("Test MAE: {model.evaluate(test_dataset)[1]:.2f}") it should be: print(f"Test MAE: {model.evaluate(test_dataset)[1]:.2f}")
There is a typo in Listing 4.29 Saving the validation logs at each fold. The correct key is mean_absolute_error and not val_mean_absolute_error
