├── Chapter_1 ├── imdb.py ├── mnist.py ├── mnist_V1.py ├── mnist_V10.py ├── mnist_V2.py ├── mnist_V3.py ├── mnist_V4.py ├── mnist_V5.py ├── mnist_V6.py ├── mnist_V7.py ├── mnist_V8.py ├── mnist_V9.py ├── simple_dense.py └── simple_model.py ├── Chapter_10 ├── bert_masked_token_inference.py └── clip_text_and_image_inference.py ├── Chapter_11 ├── DQNCartPole.ipynb ├── DQN_Atari_v2.ipynb ├── DQN_atari.py ├── Introduction_to_gym.ipynb └── random_agent_playing.ipynb ├── Chapter_12 ├── AleatoryUncertainity_using_TFP.ipynb ├── Bayesian_networks.ipynb ├── EpistemicUncertainity_using_TFP.ipynb ├── Fun_with_tensorflow_probability.ipynb └── Introduction_to_TFP.ipynb ├── Chapter_16 ├── H2o_classification.ipynb └── PyTorch.ipynb ├── Chapter_17 ├── custom_dataset.py ├── custom_layer.py ├── graph_classification.py ├── link_prediction.py └── node_classification.py ├── Chapter_19 ├── End_to_End_TFDS_pipeline.ipynb ├── Image_Classification_TF_Hub.ipynb ├── Introduction_to_TensorFlow_datasets.ipynb ├── data.js ├── index.html ├── mnist.js └── script.js ├── Chapter_2 ├── logistic_regression_using_keras_API.ipynb ├── multiple_linear_regression_using_keras_API.ipynb ├── simple_linear_regression.ipynb └── simple_linear_regression_using_keras_API.ipynb ├── Chapter_20 ├── imdb_CNN.py ├── tf_hub.py └── visual_qa.py ├── Chapter_3 ├── READ.ME ├── VGG16_implemented.py ├── cat-standing.jpg ├── cat.jpg ├── cifar10.py ├── cifar10_CNN_DEEP.py ├── cifar10_CNN_DEEP_DATA_AUGUMENT.py ├── cifar10_architecture.json ├── cifar10_predict.py ├── cifar10_weights.h5 ├── dog.jpg ├── how_to_download_vgg16_weights.h5 ├── leNet_CNN_mnist.py ├── mnist_V9.py ├── steam-locomotive.jpg ├── vgg16_features.py └── vgg16_pretrained.py ├── Chapter_4 ├── create_embedding_with_text8.py ├── elmo_from_tfhub.py ├── elmo_keraslayer.py ├── explore_text8_embedding.py ├── google_sent_enc_from_tfhub.py ├── neurips_papers_node2vec.py ├── random-walks.txt ├── spam_classifier.py ├── tf2_cbow_model.py ├── tf2_matrix_factorization.py └── tf2_skipgram_model.py ├── Chapter_5 ├── alice_text_generator.py ├── gru_pos_tagger.py ├── lstm_sentiment_analysis.py ├── peephole_lstm.py ├── seq2seq_with_attn.py └── seq2seq_wo_attn.py ├── Chapter_6 ├── HuggingFace-FineTuning.py ├── HuggingFace-Generation.py ├── HuggingFace-Ner.py ├── HuggingFace-Summarizer.py ├── READ.ME └── TFHUB_bert.py ├── Chapter_7 ├── DBN.ipynb ├── PCA.ipynb ├── k_means_using_tensorflow.ipynb ├── restricted_boltzmann_machines.ipynb └── som.ipynb ├── Chapter_8 ├── ConvolutionAutoencoder.ipynb ├── DenoisingAutoencoder.ipynb ├── SparseAutoEncoder.ipynb ├── VAE.ipynb ├── VanillaAutoEncoder.ipynb └── sentence_vector_gen.ipynb ├── Chapter_9 ├── CycleGAN_TF2.ipynb ├── DCGAN.ipynb └── VanillaGAN.ipynb ├── Cover.png ├── LICENSE └── README.md /Chapter_1/imdb.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Deep-Learning-with-TensorFlow-and-Keras-3rd-edition/HEAD/Chapter_1/imdb.py 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