Dive into Deep Learning

TL;DR

This open-source book aims to make deep learning accessible by combining theoretical concepts, practical code examples, and interactive content within Jupyter notebooks, fostering community engagement and continuous updates.

Contribution

It provides a comprehensive, freely available resource that integrates explanations, code, and interactive elements to teach deep learning effectively.

Findings

Accessible deep learning resource with integrated code and explanations

Supports community-driven updates and discussions

Balances technical depth with practical application

Abstract

This open-source book represents our attempt to make deep learning approachable, teaching readers the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code. Our goal is to offer a resource that could (i) be freely available for everyone; (ii) offer sufficient technical depth to provide a starting point on the path to actually becoming an applied machine learning scientist; (iii) include runnable code, showing readers how to solve problems in practice; (iv) allow for rapid updates, both by us and also by the community at large; (v) be complemented by a forum for interactive discussion of technical details and to answer questions.