There are few books, in recent memory, that I have read that have evoked such a joy and sense of excitment in the process of learning new material. Imagine the sense, as you read, of a master-teacher guiding you step-by-step.
Francois Chollet's recent contribution of "Deep Learning with Python, 1st Edition", through Manning Publications, is a demonstration of a world-class educator combined with the depth and experience of deep subject matter knowledge.
Chollet's attention to detail, conciseness, and ability to communicate and build the reader's knowledge in successive layers are demonstrated again and again - throughout each chapter.
The structure of the book's chapters should give you an appreciation for the skillful writing needed to cover such a breadth of material in 352 pages.
Part I: Fundamentals of Deep Learning
1 - What is Deep Learning
2 - Before we begin: the mathematical building blocks of neural networks
3 - Getting started wtih neural networks
4 - Fundamentals of machine learning
Part II: Deep Learning in Pratice
5 - Deep learning in computer vision
6 - Deep learning for text and sequences
7 - Advanced deep-learning best practices
8 - Generative deep learning
9 - Conclusions
Appendix A - Installing Keras and its dependencies on Ubuntu
Appendix B - Running Jupyter notebooks on an EC2 GPU instance
This book is such an exceptional value for your money spent - that I would urge any organization to buy copies for your engineers, architects, and data analytics teams today. It is an excellent introductory book on Deep Learning - that will provide a solid foundation for your teams to build upon going forward.
Full Disclosure: Manning provided me a copy to review.
https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438/
