[Paper Review] Dive into Deep Learning
A openly developable, open-source book on deep learning drafted in Jupyter notebooks, combining explanations, math, interactive examples, and runnable code for applied ML readers.
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.
Motivation & Objective
- Provide an accessible, technically deep introduction to deep learning.
- Offer runnable code and interactive demonstrations to enable practical learning.
- Enable rapid updates and community-driven discussion around technical details.
Proposed method
- Content is organized as Jupyter notebooks integrating exposition, figures, math, and runnable code.
- Code and data are included to solve practical deep learning problems.
- Designed for free accessibility and ongoing community contributions and discussion.
Better researchstarts right now
From reading papers to final review, dramatically reduce your research time.
No credit card · Free plan available
This review was created by AI and reviewed by human editors.