TensorFlow Book

Spans from TensorFlow basics like tensors and sessions to advanced topics such as CNNs, RNNs, and reinforcement learning across 12 chapters, as outlined in the README.

Provides practical implementations for each concept, including linear regression in Chapter 3 and autoencoders in Chapter 7, reinforcing learning by doing.

Organized by the book's chapters, ensuring code examples directly complement the theoretical explanations in 'Machine Learning with TensorFlow' for a cohesive learning experience.

Includes TensorBoard usage in Chapter 2 for model visualization, aiding in debugging and understanding machine learning models effectively.