Preface Chapter 1： Getting Started with TensorFlow Introduction How TensorFlow Works Declaring Tensors Using Placeholders and Variables Working with Matrices Declaring Operations Implementing Activation Functions Working with Data Sources Additional Resources Chapter 2：The TensorFlow Way Introduction Operations In a Computational Graph Layering Nested Operations Working with Multiple Layers Implementing Loss Functions Implementing Back Propagation Working with Batch and Stochastic Training Combining Everything Together Evaluating Models Chapter 3： Linear Regression Introduction Using the Matrix Inverse Method Implementing a Decomposition Method Learning The TensorFlow Way of Linear Regression Understanding Loss Functions In Linear Regression Implementing Deming regression Implementing Lasso and Ridge Regression Implementing Elastlc Net Regression Implementing Logistic Regression Chapter 4： Support Vector Machines Introduction Working with a Linear SVM Reduction to Linear Regression Working with Kernels in TensorFlow Implementing a Non—Linear SVM Implementing a Multi—Class SVM Chapter 5： Nearest Neighbor Methods Introduction Working with Nearest Neighbors Working with Text—Based Distances Computing with Mixed Distance Functions Using an Address Matching Example Using Nearest Neighbors for Image Recognition Chapter 6： Neural Networks Introduction Implementing Operational Gates Working with Gates and Activation Functions Implementing a One—Layer Neural Network Implementing Different Layers Using a Multilayer Neural Network Improving the Predictions of Linear Models Learning to Play Tic Tac Toe Chapter 7： Natural Language Processing Introduction Working with bag of words Implementing TF—IDF Working with Skip—gram Embeddings Working with CBOW Embeddings Making Predictions with Word2vec Using Doc2vec for Sentiment Analysis Chapter 8： Convolutional Neural Networks Introduction Implementing a Simpler CNN Implementing an Advanced CNN Retraining Existing CNNs models Applying Stylenet／Neural—Style Implementing DeepDream Chapter 9： Recurrent Neural Networks Introduction Implementing RNN for Spam Prediction Implementing an LSTM Model Stacking multiple LSTM Layers Creatlng Sequence—to—Sequence Models Training a Siamese Similarity Measure Chapter 10： Taking TensorFlow to Production Introduction Implementing unit tests Using Multiple Executors Parallelizing TensorFlow Taking TensorFlow to Production Productionalizing TensorFlow—An Example Chapter 11： More with TensorFlow Introduction Visualizing graphs in Tensorboard There's more... Working with a Genetic Algorithm Clustering Using K—Means Solving a System of ODEs Index
