　　TensorFlow是一个用于机器智能的开源软件库。书中的每一个实例都会教你如何使用TensorFlow应对复杂的数据计算，使你比以前更深入的探究数据，加深对于数据的认识。这些实例涵盖了模型训练、模型评估、情感分析、回归分析、聚类分析、人工神经网络以及深度学习，每一个都用到了Google的机器学习库TensorFlow。
　　《TensorFlow机器学习攻略（英文影印版）》首先介绍了TensorFlow的基础知识，其中包括变量、矩阵以及各种数据源。然后你将使用TensorFlow来学习线性回归技术。剩下的部分涵盖了其他一些重要的高级概念，如神经网络、CNN、RNN和NLP。
　　当你熟悉并适应了TensorFlow的生态系统，《TensorFlow机器学习攻略（英文影印版）》最后一章将为你展示如何将其应用到产品中。

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Preface
Chapter 1： GettingStarted with TensorFlow
Introduction
How TensorFIow 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 TensorFIow Way of Linear Regression
Understanding Loss Functions in Linear Regression
Implementing Deming regression
Implementing Lasso and Ridge Regression
Implementing Elastic Net Regression
Implementing Logistic Regression

Chapter 4： Support Vector Machines
Introduction
Working with a Linear SVM
Reduction to Linear Regression
Working with Kernels in TensorFIow
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/NeuraI-Style
Implementing DeepDream

Chapter 9： Recurrent Neural Networks
Introduction
Implementing RNN for Spam Prediction
Implementing an LSTM Model
Stacking multiple LSTM Layers
Creating Sequence-to-Sequence Models
Training a Siamese Similarity Measure

Chapter 10： Taking TensorFIow to Production
Introduction
Implementing unit tests
Using Multiple Executors
Parallelizing TensorFIow
Taking TensorFIow to Production
Productionalizing TensorFIow - An Example

Chapter 11： More with TensorFIow
Introduction
Visualizing graphs in Tensorboard
There's more．．．
Working with a Genetic Algorithm
Clustering Using K-Means
Solving a System of ODEs
Index
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