Understanding Deep Learning
by Simon J. D. Prince · first published 2023
93% recommend7% neutral
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I second the recommendation for Peter Bloem’s tutorial. I’m also about to read the transformer chapter from this excellent upcoming book by Simon Prince: udlbook https://udlbook.github.io/udlbook/
on “The Transformer Family” ↗I think "Understanding Deep Learning" is very nice - https://udlbook.github.io/udlbook/ (an covers almost all topics, it has maybe just a couple of omissions, such as Multimodal Learning, NERFs and Time Series Prediction)
on “Probabilistic Machine Learning: Advanced Topics” ↗Depending on your math background, i would go for Bishop's Deep Learning Foundations and Concepts and Simon Prince's Understanding Deep Learning.
on “Ask HN: Resources to brush up from 'Intro to ML' to current LLMs/generative AI?” ↗The "Understanding Deep Learning" book covers more recent models as well: https://udlbook.github.io/udlbook/ (free PDF and Jupyter notebooks available)
on “Deep Learning Course” ↗Lots of great resources listed here. But I think there is "Understanding Deep Learning" missing from the list [0]. In my opinion, Simon J.D. Prince accomplished a true feat with his book, not only through the material itself but also with the notes attached to each chapter, linking directly to advanced references (free literature review), exercises that really challenge your understanding of the m
on “Deep Learning Course” ↗https://udlbook.github.io/udlbook/ This is a good beginner resource that goes over broad concepts with Jupyter Notebook examples, once you get through this I recommend looking through forums for more advanced topics that appeal to you
on “Ask HN: Artificial Intelligence resources/path from beginner to advance” ↗The book that just came out, "Understanding Deep Learning", is an excellent overview of the current state of AI: https://udlbook.github.io/udlbook/ Read that first, then to keep up to date you can follow up with any papers that seem interesting to you. A good way to be aware of the interesting papers that come out is to follow @_akhaliq on X: https://twitter.com/_akhaliq
on “Ask HN: AI/ML papers to catch up with current state of AI?” ↗…Pedro Domingo Fresh look (books published in 2023) - The Worlds I See: Curiosity, Exploration, and Discovery at the Dawn of AI by Fei-Fei Li - The Coming Wave by Mustafa Suleyman and Michael Bhaskar - Understanding Deep Learning by Simon J. D. Prince History of Artificial Intelligence and Machine Learning - The Turing Guide by Jack Copeland, Jonathan Bowen, Mark Sprevak, Robin Wilson - The Quest for Artificial Intelligence:…
on “Books about AI&ML for your holiday reading” ↗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.statlearnin
on “Ask HN: What's the best way to learn machine learning in 2024?” ↗„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…
on “Ask HN: What is the best thing you read in 2024?” ↗I would recommend https://udlbook.github.io/udlbook/ instead if you're looking to learn about modern generative AI.
on “Understanding Machine Learning: From Theory to Algorithms” ↗https://udlbook.github.io/udlbook/
on “Ask HN: What's the best way to learn machine learning in 2024?” ↗https://udlbook.github.io/udlbook/
on “Ask HN: How to learn AI from first principles?” ↗Start here: https://udlbook.github.io/udlbook/
on “LLMs should not replace therapists” ↗…For those who feel similarly, I asked about it recently, maybe something you like the look of: https://news.ycombinator.com/item?id=38320244 I also came across (doesn't seem it was mentioned there) Understanding Deep Learning (Simon Prince) which looks like it might be good: https://mitpress.mit.edu/9780262048644/understanding-deep-le...
on “Ask HN: Daily practices for building AI/ML skills?” ↗