Introduction
Chapter 1: Modeling Procedure of TensorFlow
1-1 Example: Modeling Procedure for Structured Data
1-2 Example: Modeling Procedure for Images
1-3 Example: Modeling Procedure for Texts
1-4 Example: Modeling Procedure for Temporal Sequences
Chapter 2: Key Concepts of TensorFlow
2-1 Data Structure of Tensor
2-2 Three Types of Graph
2-3 Automatic Differentiate
Chapter 3: Hierarchy of TensorFlow
3-1 Low-level API: Demonstration
3-2 Mid-level API: Demonstration
3-3 High-level API: Demonstration
Chapter 4: Low-level API in TensorFlow
4-1 Structural Operations of the Tensor
4-2 Mathematical Operations of the Tensor
4-3 Rules of Using the AutoGraph
4-4 Mechanisms of the AutoGraph
4-5 AutoGraph and tf.Module
Chapter 5: Mid-level API in TensorFlow
5-1 Dataset
5-2 feature_column
5-3 activation
5-4 layers
5-5 losses
5-6 metrics
5-7 optimizers
5-8 callbacks
Chapter 6: High-level API in TensorFlow
6-1 Three Ways of Modeling
6-2 Three Ways of Training
6-3 Model Training Using Single GPU
6-4 Model Training Using Multiple GPUs
6-5 Model Training Using TPU
6-6 Model Deploying Using tensorflow-serving
6-7 Call Tensorflow Model Using spark-scala
Epilogue：A Story Between a Foodie and Cuisine
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1. 开源生态
2. 协作、人、软件
3. 评估模型
