Dive into Deep Learning
An approachable text combining the depth and quality of a textbook with the interactive multi-framework code of a hands-on tutorial.
Stats for nerds
30recommend
42mentions
35people
2019–2025years
86% recommend12% neutral2% critical
Mentions per year
What Hacker News says
- Try Andrej Karpathy's YouTube videos. I also really liked the Dive into Deep Learning book at d2l.ai
- I believe that most of the papers presented here focus on acquiring knowledge rather than deep understanding. If you’re completely unfamiliar with the subject, I recommend starting with textbooks rather than papers. The latest Bishop’s "Deep Learning: Foundations and Concepts (2024)" [1] is an excellent resource that covers the "basics" of deep learning and is quite updated. Another good option is Chip Huyen’s "AI Engineering (2024)" [2]. Another excellent choice will be "Dive into Deep Learning" [3], Understanding Deep Learning [4], or just read anything from fast.ai and watch Karpathy's lectures on YouTube.
[1]: https://www.bishopbook.com [2]: https://www.oreilly.com/library/view/ai-engineering/97810981... [3]: https://d2l.ai [4]: https://udlbook.github.io/udlbook/
- A variant of this question seems to get asked every 6 mo. but so far, I haven't seen this question tackled directly: If I want to learn the concepts and fundamentals of AI from first principles, what educational resources should I use?
I'm not interested in hands-on guides (eg. how to train a DNN classifier in TensorFlow) or LLM-centric resources.
So far, I've put together the following curriculum:
1 Artificial Intelligence: A Modern Approach (https://aima.cs.berkeley.edu/) - Great for learning the breadth of foundational concepts, eg. local search algorithms, building up to modern AI.
2 Probabilistic Machine Learning: An Introduction (https://probml.github.io/pml-book/book1.html) - Going more in-depth into ML.
3 Dive into Deep Learning (https://d2l.ai/) - Going deep into DL, including contemporary ideas like Transformers and Diffusion models.
4. Neural networks and Deep Learning (http://neuralnetworksanddeeplearning.com/) could also be a great resource but the content probably overlaps significantly with 3.
Would anybody add/update/remove anything? (Don't have to limit recommendations to textbooks. Also open to courses, papers, etc.)
Sorry for the semi-redundant post.
- As a starting point https://d2l.ai/chapter_preliminaries/index.html and https://d2l.ai/chapter_appendix-mathematics-for-deep-learnin... is enough.
- Hey godelski, thanks for actually taking the time to clarify on this and it's always great to be taken seriously. I have not really much to add, but i would love to see your energy in educating the people that are commenting here.
I like this page a lot: https://d2l.ai/chapter_preliminaries/calculus.html And i also learned a lot from ben eater (Youtube and this: https://eater.net/quaternions) Category theory was a godsend from Milewski, esp. his PDF is great! https://bartoszmilewski.com/2014/10/28/category-theory-for-p...
Maybe you can add a little and offer ressources from your background?
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