Understanding Deep Learning
An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice. Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics.
Stats for nerds
14recommend
15mentions
15people
2023–2025years
93% recommend7% neutral
Mentions per year
What Hacker News says
- Start here:
- I would recommend https://udlbook.github.io/udlbook/ instead if you're looking to learn about modern generative AI.
- „Understanding Deep Learning” by Simon Prince
Among all ML books, it’s a really good overview of many concepts connected one to another and spelled out in easy to grasp way
Also „Designing Machine Learning Systems” by Chip Huyen looks really promising and very hands-on (lessons learned by very committed practitioner). It gives actionable tips and provides real life insights. I haven’t finished reading it yet though
- Assuming you already know some basic linear algebra and calculus, know Python (or R), and have a decent-but-not-advanced grasp of statistics, I'd recommend working through these books. They are very readable and focus on intuitive understanding/practical applications, but give enough technical foundation for you to jump into more specific subfields if needed.
Stats & ML - https://www.statlearning.com/
Deep Learning - https://udlbook.github.io/udlbook/
Reinforcement Learning - https://web.stanford.edu/class/psych209/Readings/SuttonBarto...
As with anything else, people usually fail to learn ML not because of content quality but because of lack of effort/time/consistency. Take handwritten notes, solve exercises, etc., and expect to spend at least a hundred hours on each book.
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