Summary 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