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Jupyter notebooks for the code samples of the book "Deep Learning with Python"

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Following the example in the book, I increased the sample size from 200 to 3000 and added a dropout regularization, but the final validation accuracy is only 50%. ![1661074009976](https://user-images.githubusercontent.com/74046524/185784611-5bb7c690-60f1-4530-a2d6-524572cfa46e.png)

Hello, I love this collection of notebooks! The book is great! One thing. When using the GloVe embeddings, there seems to be a flaw in the implementation: When creating the...

In the `chapter11_part04_sequence-to-sequence-learning.ipynb`, the TransformerDecoder receives the mask from the PositionalEmbedding layer of the target sequence: ``` x = PositionalEmbedding(sequence_length, vocab_size, embed_dim)(decoder_inputs) x = TransformerDecoder(embed_dim, dense_dim, num_heads)(x, encoder_outputs) ``` Shouldn’t...

# Notebook 3.5-classifying-movie-reviews The code that is supposed to generate the Training and validation loss side by side uses wrong history.history keys: `acc = history.history['acc']` `val_acc = history.history['val_acc']` `loss =...

You already called the standardize() function in the tokenize() function. ``` def tokenize(self, text): text = self.standardize(text) return text.split() ``` Is it necessary to call it again before the tokenize()...

ImportError: Could not import PIL.Image. The use of `array_to_img` requires PIL. Assume the train_dir path is not correct. How can i fix it?

Hello, can you help me please. When I try to check my result on a test samples. I have an error. My code same as yours from listings 5.17 to...

```python model.compile(optimizer="rmsprop",loss="sparse_categorical_crossentropy") callbacks ={ keras.callbacks.ModelCheckpoint("oxford_segmentation.keras",save_best_only=True) } history = model.fit(train_input_imgs, train_targets, epochs=50, callbacks=callbacks, batch_size=64, validation_data=(val_input_imgs, val_targets)) epochs =range(1,len(history.history["loss"]) +1) loss =history.history["loss"] val_loss =history.history["val_loss"] plt.figure() plt.plot(epochs,loss,"bo",label="Training loss") plt.plot(epochs,val_loss,"b",label="Validation loss") plt.title("Training and validation...

The 24th code cell raises the following error when it runs on a PC using Anaconda(Python 3.8.5 + Tensorflow 2.6 or 2.7) while it runs well on Google Colab. ```python...