├── 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 ├── DBN.ipynb ├── Kmeans.ipynb ├── PCA.ipynb ├── Restricted_boltzmann_Machines_MNIST.ipynb ├── Self_Organized_Maps.ipynb ├── VAE.ipynb └── colors.csv ├── Chapter 11 ├── DQNCartPole.ipynb ├── DQN_Atari_v2.ipynb ├── DQN_atari.py ├── Gym_introduction.ipynb └── random_agent_playing.ipynb ├── Chapter 2 ├── GradientTape.py ├── autograph.py ├── autograph_timing.py ├── batch_datasets.py ├── computational_graph.py ├── datasets.py ├── distribute_strategy.online ├── estimators.py ├── mirrored_strategy.py ├── model.png ├── shared_layers.py └── shared_model.png ├── Chapter 3 ├── MNIST_using_estimators.ipynb ├── boston_house_prediction.ipynb ├── multiple_linear_regression.ipynb └── simple_linear_regression.ipynb ├── Chapter 4 ├── 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 5 ├── distribute_strategy.ipynb ├── horse_and_humans_transfer_learning.ipynb ├── imdb_CNN.py ├── tf_hub.py └── visual_qa.py ├── Chapter 6 ├── CycleGAN_TF2.ipynb ├── DCGAN.ipynb ├── DCGAN_MNIST.ipynb └── VanillaGAN.ipynb ├── Chapter 7 ├── bert_paraphrase.py ├── create_embedding_with_text8.py ├── elmo_from_tfhub.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 8 ├── alice_text_generator.py ├── gru_pos_tagger.py ├── 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