Top Deep Learning books recommended by experts

A generative model is a type of machine learning model that aims to learn the underlying patterns or distributions of data in order to generate new, similar data. In essence, it's like teaching a computer to dream up its own data based on what it has seen before.

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”

At 100 pages (or a little more), the book is short enough to read in a single sitting. Yet, despite its length, it covers all the major machine learning approaches, ranging from classical linear and logistic regression, through to modern support vector machines, deep learning, boosting, and random forests.

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.

Artificial Intelligence: A Guide for Thinking Humans provides readers with an accessible, entertaining, and clear-eyed view of the AI landscape, what the field has actually accomplished, how much further it has to go, and what it means for all of our futures.

Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks.