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