├── Chapter01 └── 01_Introduction │ ├── 01_How_TensorFlow_Works │ └── 01_How_TensorFlow_Works.ipynb │ ├── 02_Creating_and_Using_Tensors │ ├── 02_tensors.ipynb │ └── 02_tensors.py │ ├── 03_Using_Variables_and_Placeholders │ ├── 03_placeholders.ipynb │ └── 03_placeholders.py │ ├── 04_Working_with_Matrices │ ├── 04_matrices.ipynb │ └── 04_matrices.py │ ├── 05_Declaring_Operations │ ├── 05_operations.ipynb │ └── 05_operations.py │ ├── 06_Implementing_Activation_Functions │ ├── 06_activation_functions.ipynb │ └── 06_activation_functions.py │ ├── 07_Working_with_Data_Sources │ ├── 07_data_gathering.ipynb │ ├── 07_data_gathering.py │ └── birthweight_data │ │ └── birthweight.dat │ └── images │ ├── 01_outline.png │ ├── 02_variable.png │ ├── 03_placeholder.png │ ├── 06_activation_funs1.png │ └── 06_activation_funs2.png ├── Chapter02 └── 02_TensorFlow_Way │ ├── 01_Operations_as_a_Computational_Graph │ ├── 01_operations_on_a_graph.ipynb │ └── 01_operations_on_a_graph.py │ ├── 02_Layering_Nested_Operations │ ├── 02_layering_nested_operations.ipynb │ └── 02_layering_nested_operations.py │ ├── 03_Working_with_Multiple_Layers │ ├── 03_multiple_layers.ipynb │ └── 03_multiple_layers.py │ ├── 04_Implementing_Loss_Functions │ ├── 04_loss_functions.ipynb │ └── 04_loss_functions.py │ ├── 05_Implementing_Back_Propagation │ ├── 05_back_propagation.ipynb │ └── 05_back_propagation.py │ ├── 06_Working_with_Batch_and_Stochastic_Training │ ├── 06_batch_stochastic_training.ipynb │ └── 06_batch_stochastic_training.py │ ├── 07_Combining_Everything_Together │ ├── 07_combining_everything_together.ipynb │ └── 07_combining_everything_together.py │ ├── 08_Evaluating_Models │ ├── 08_evaluating_models.ipynb │ └── 08_evaluating_models.py │ └── images │ ├── 01_Operations_on_a_Graph.png │ ├── 02_Multiple_Operations.png │ ├── 03_Multiple_Layers.png │ ├── 04_loss_fun1.png │ ├── 04_loss_fun2.png │ ├── 06_Back_Propagation.png │ ├── 07_Combing_Everything_Together.png │ └── 08_Evaluating_Models.png ├── Chapter03 └── 03_Linear_Regression │ ├── 01_Using_the_Matrix_Inverse_Method │ ├── 01_lin_reg_inverse.ipynb │ └── 01_lin_reg_inverse.py │ ├── 02_Implementing_a_Decomposition_Method │ ├── 02_lin_reg_decomposition.ipynb │ └── 02_lin_reg_decomposition.py │ ├── 03_TensorFlow_Way_of_Linear_Regression │ ├── 03_lin_reg_tensorflow_way.ipynb │ └── 03_lin_reg_tensorflow_way.py │ ├── 04_Loss_Functions_in_Linear_Regressions │ ├── 04_lin_reg_l1_vs_l2.ipynb │ └── 04_lin_reg_l1_vs_l2.py │ ├── 05_Implementing_Deming_Regression │ ├── 05_deming_regression.py │ └── 05_demming_regression.ipynb │ ├── 06_Implementing_Lasso_and_Ridge_Regression │ ├── 06_lasso_and_ridge_regression.ipynb │ └── 06_lasso_and_ridge_regression.py │ ├── 07_Implementing_Elasticnet_Regression │ ├── 07_elasticnet_regression.ipynb │ └── 07_elasticnet_regression.py │ ├── 08_Implementing_Logistic_Regression │ ├── 08_logistic_regression.ipynb │ └── 08_logistic_regression.py │ └── images │ ├── 01_Inverse_Matrix_Method.png │ ├── 02_Cholesky_Decomposition.png │ ├── 03_lin_reg_fit.png │ ├── 03_lin_reg_loss.png │ ├── 04_L1_L2_learningrates.png │ ├── 04_L1_L2_loss.png │ ├── 04_L1_L2_loss2.png │ ├── 05_demming_reg.png │ ├── 05_demming_vs_linear_reg.png │ ├── 07_elasticnet_reg_loss.png │ ├── 08_logistic_reg_acc.png │ └── 08_logistic_reg_loss.png ├── Chapter04 └── 04_Support_Vector_Machines │ ├── 02_Working_with_Linear_SVMs │ ├── 02_linear_svm.ipynb │ └── 02_linear_svm.py │ ├── 03_Reduction_to_Linear_Regression │ ├── 03_support_vector_regression.ipynb │ └── 03_support_vector_regression.py │ ├── 04_Working_with_Kernels │ ├── 04_svm_kernels.ipynb │ └── 04_svm_kernels.py │ ├── 05_Implementing_Nonlinear_SVMs │ ├── 05_nonlinear_svm.ipynb │ └── 05_nonlinear_svm.py │ ├── 06_Implementing_Multiclass_SVMs │ ├── 06_multiclass_svm.ipynb │ └── 06_multiclass_svm.py │ └── images │ ├── 01_introduction.png │ ├── 02_linear_svm_accuracy.png │ ├── 02_linear_svm_loss.png │ ├── 02_linear_svm_output.png │ ├── 03_linear_svm_loss.png │ ├── 03_svm_regression_output.png │ ├── 04_linear_svm_gaussian.png │ ├── 04_nonlinear_data_linear_kernel.png │ ├── 05_non_linear_svms.png │ └── 06_multiclass_svm.png ├── Chapter05 └── 05_Nearest_Neighbor_Methods │ ├── 02_Working_with_Nearest_Neighbors │ ├── 02_nearest_neighbor.ipynb │ └── 02_nearest_neighbor.py │ ├── 03_Working_with_Text_Distances │ ├── 03_text_distances.ipynb │ └── 03_text_distances.py │ ├── 04_Computing_with_Mixed_Distance_Functions │ ├── 04_mixed_distance_functions_knn.ipynb │ └── 04_mixed_distance_functions_knn.py │ ├── 05_An_Address_Matching_Example │ ├── 05_address_matching.ipynb │ └── 05_address_matching.py │ ├── 06_Nearest_Neighbors_for_Image_Recognition │ ├── 06_image_recognition.ipynb │ └── 06_image_recognition.py │ └── images │ ├── 02_mse_vs_variance.png │ ├── 02_nn_histogram.png │ ├── 04_pred_vs_actual.png │ ├── 06_nn_image_recognition.png │ ├── image.png │ └── nearest_neighbor_intro.jpg ├── Chapter06 └── 06_Neural_Networks │ ├── 01_Introduction │ └── Introduction_to_Neural_Networks.ipynb │ ├── 02_Implementing_an_Operational_Gate │ ├── 02_gates.ipynb │ └── 02_gates.py │ ├── 03_Working_with_Activation_Functions │ ├── 03_activation_functions.ipynb │ └── 03_activation_functions.py │ ├── 04_Single_Hidden_Layer_Network │ ├── 04_single_hidden_layer_network.ipynb │ └── 04_single_hidden_layer_network.py │ ├── 05_Implementing_Different_Layers │ ├── 05_implementing_different_layers.ipynb │ └── 05_implementing_different_layers.py │ ├── 06_Using_Multiple_Layers │ ├── 06_using_a_multiple_layer_network.ipynb │ └── 06_using_a_multiple_layer_network.py │ ├── 07_Improving_Linear_Regression │ ├── 07_improving_linear_regression.ipynb │ └── 07_improving_linear_regression.py │ ├── 08_Learning_Tic_Tac_Toe │ ├── base_tic_tac_toe_moves.csv │ └── tic_tac_toe_moves.py │ └── images │ ├── 02_operational_gates.png │ ├── 03_activation1.png │ ├── 03_activation2.png │ ├── 04_nn_layout.png │ ├── 04_nn_loss.png │ ├── 06_nn_multiple_layers_loss.png │ ├── 07_lin_reg_acc.png │ ├── 07_lin_reg_loss.png │ ├── 08_tic_tac_toe_architecture.png │ ├── 08_tictactoe_layout.png │ ├── 08_tictactoe_loss.png │ └── image.png ├── Chapter07 └── 07_Natural_Language_Processing │ ├── 01_Introduction │ └── 01_introduction.ipynb │ ├── 02_Working_with_Bag_of_Words │ ├── 02_bag_of_words.ipynb │ └── 02_bag_of_words.py │ ├── 03_Implementing_tf_idf │ ├── 03_implementing_tf_idf.ipynb │ └── 03_implementing_tf_idf.py │ ├── 05_Working_With_CBOW_Embeddings │ ├── 05_Working_With_CBOW.ipynb │ ├── 05_Working_With_CBOW.py │ └── text_helpers.py │ ├── 06_Using_Word2Vec_Embeddings │ ├── 06_using_word2vec.ipynb │ ├── 06_using_word2vec.py │ └── text_helpers.py │ ├── 07_Sentiment_Analysis_With_Doc2Vec │ └── text_helpers.py │ └── images │ ├── 02_bag_of_words.png │ ├── 03_tfidf_acc.png │ ├── 03_tfidf_loss.png │ ├── 04_skipgram_model.png │ ├── 05_cbow_model.png │ ├── 06_word2vec_acc.png │ ├── 06_word2vec_loss.png │ ├── 07_sentiment_doc2vec_loss.png │ └── image.png ├── Chapter08 └── 08_Convolutional_Neural_Networks │ ├── 02_Intro_to_CNN_MNIST │ ├── 02_introductory_cnn.ipynb │ └── 02_introductory_cnn.py │ ├── 03_CNN_CIFAR10 │ ├── 03_cnn_cifar10.ipynb │ └── 03_cnn_cifar10.py │ ├── 04_Retraining_Current_Architectures │ └── 04_download_cifar10.py │ ├── 05_Stylenet_NeuralStyle │ ├── 05_stylenet.ipynb │ └── 05_stylenet.py │ ├── 06_Deepdream │ ├── 06_deepdream.ipynb │ ├── 06_deepdream.py │ └── book_cover.jpg │ └── images │ ├── 01_intro_cnn.png │ ├── 01_intro_cnn2.png │ ├── 02_cnn1_loss_acc.png │ ├── 02_cnn1_mnist_output.png │ ├── 03_cnn2_loss_acc.png │ ├── 05_stylenet_ex.png │ ├── 06_deepdream_ex.png │ ├── book_cover.jpg │ ├── image.png │ └── starry_night.jpg ├── Chapter09 └── 09_Recurrent_Neural_Networks │ ├── 01_Introduction │ └── readme.md │ ├── 02_Implementing_RNN_for_Spam_Prediction │ └── 02_implementing_rnn.py │ ├── 03_Implementing_LSTM │ ├── 03_implementing_lstm.ipynb │ └── 03_implementing_lstm.py │ ├── 04_Stacking_Multiple_LSTM_Layers │ └── 04_stacking_multiple_lstm.py │ ├── 05_Creating_A_Sequence_To_Sequence_Model │ ├── 05_seq2seq_translation.ipynb │ ├── 05_seq2seq_translation.py │ └── 05_translation_model_sample.py │ ├── 06_Training_A_Siamese_Similarity_Measure │ ├── 06_siamese_similarity_driver.ipynb │ ├── 06_siamese_similarity_driver.py │ └── siamese_similarity_model.py │ └── images │ ├── 01_RNN_Seq2Seq.png │ ├── 01_RNN_Single_Target.png │ ├── 02_RNN_Spam_Acc_Loss.png │ ├── 03_LSTM_Loss.png │ ├── 04_MultipleLSTM_Loss.png │ ├── 04_MultipleRNN_Architecture.png │ ├── 05_Seq2Seq_Loss.png │ ├── 06_Similarity_RNN.png │ ├── 06_Similarity_RNN_Architecture.png │ └── 06_Similarity_RNN_Diagram.png ├── Chapter10 └── 11_Taking_TensorFlow_to_Production │ ├── 01_Implementing_Unit_Tests │ └── 01_implementing_unit_tests.py │ ├── 02_Using_Multiple_Devices │ └── 02_using_multiple_devices.py │ ├── 03_Parallelizing_TensorFlow │ └── 03_parallelizing_tensorflow.py │ ├── 04_Production_Tips │ └── 04_production_tips_for_tf.py │ ├── 05_Production_Example │ ├── 05_production_ex_eval.py │ └── 05_production_ex_train.py │ ├── 06_Using_TensorFlow_Serving │ ├── 06_Using_TensorFlow_Serving_Client.py │ └── 06_Using_TensorFlow_Serving_Train.py │ └── images │ ├── file_structure.jpg │ └── image.png ├── Chapter11 └── 12_More_with_TensorFlow │ ├── 01_Visualizing_Computational_Graphs │ └── 01_using_tensorboard.py │ ├── 02_Working_with_a_Genetic_Algorithm │ └── 02_genetic_algorithm.py │ ├── 03_Clustering_Using_KMeans │ └── 03_k_means.py │ ├── 04_Solving_A_System_of_ODEs │ └── 04_solving_ode_system.py │ ├── 05_Using_a_Random_Forest │ └── 05_Using_a_Random_Forest.py │ ├── 06_Using_TensorFlow_with_Keras │ └── 06_Using_TensorFlow_with_Keras.py │ └── images │ ├── 01_tensorboard1.png │ ├── 01_tensorboard2.png │ ├── 01_tensorboard3.png │ ├── 02_genetic_algorithm.png │ ├── 03_kmeans.png │ └── 04_ode_system.png ├── LICENSE └── README.md 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