Deep Learning with Python by François Chollet
I occasionally give talks on deep learning, and I know how important it is to present the material in the right way — practical and easy to grasp. I was recently impressed by a new book on deep learning, and I decided to share it here.
The book is called “Deep Learning with Python”. The title would not catch my attention, if not the fact that the book is written by François Chollet. François Chollet is the author of Keras. Keras is an open-source deep learning framework for Python. Because of it’s beautiful simplicity, it is extremely popular: over two hundred thousand users, which is more than one third of all the deep learning engineers in the world.
“Deep Learning with Python” is a gem. I envy those who are just about to read it. It takes you through the journey of deep learning in a very captivating way. Here are the two things that I think make the book stand out compared to other deep learning books:
1. Easy. The material is targeted to anyone with basic Python coding experience. Mathematical concepts are explained using code snippets. No need to dive into math notation or equations, which can be inscrutable for people without a relevant degree.
2. Practical and advanced. Typically, advanced theoretical books are disconnected from concrete problems, and practical books are superficial.
In a solid way, the book covers the topics of:
- Fundamentals of machine learning and neural networks,
- Setting up your environment for practical work — both software and hardware,
- Deep learning for computer vision — convolution operation is nicely explained,
- Deep learning for text and sequences — both RNNs and CNNs, with advice on algorithmic trading (“don’t”),
- Advanced deep-learning best practices — ways to be more in control,
- Generative deep learning — both text and images (including “the smile vector”).
If you are interested in starting strong with deep learning, you will enjoy this book: https://www.manning.com/books/deep-learning-with-python
Read the original review here.
