A group of scientists from Amazon has published a new book on deep learning that is supposed to help students and developers learn about machine learning and deployment of machine learning models in production.
The book, named “Dive into Deep Learning” is an open-source, interactive book that combines theory of deep learning with hands-on examples and code, and which also describes the underlying mathematics behind deep learning.
The start of the book goes back in 2017 when researchers wanted to provide open-source material for learning deep learning through practical examples and code. According to the writers, one reason for starting to write the book was the request from students at Carnegie Mellon University to turn the lecture notes and materials into a textbook.
Several years later, “Dive into Deep Learning” was completed as a free book on deep learning which features Jupyter notebooks as interactive content on practical applications of deep learning models. The authors of the book put an emphasis on “learning by doing” and they mention that the book is easy to follow and it is intended for self-study.
The book was first published in Chinese and then translated into English. It can be found on Github, and readers are encouraged to suggest changes and new content in order to improve the quality of the book.
