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Comment by gozzoo | Hacker News Reader

This book seems to be very theoretical. Can someone recoment more practical books with code samples using some modern ML framework, probably something like _Hands-On Machine Learning_ by Geron Aurelien

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To be honest I think this may be actually an advantage: it explains concepts which otherwise are just weird parameters in code. Since it is pretty lengthy I would actually recommend to read the chapters relevant to a specific method you are interested in (maybe going a backwards to build the right context).

If you I'd combine it with e.g. https://spinningup.openai.com/en/latest/ or doing some toy projects with https://stable-baselines3.readthedocs.io/en/master/ it would probably render the most value.

I second this. When I started learning ML, rather than getting into coding I first watched Yaser Abu-Mostafa's Caltech lectures on YouTube diligently. I used his book as a companion. Next I watched Robert Tibshirani's lectures. I was interested in learning the fundamentals, which helped me a lot when I started coding.

I made a small project where I show how to train a Neural Net with Q-Learning for a simple temperature control problem. Still a hard read for someone with no background in math/control theory probably, but the code is actually much easier than the text^^

https://github.com/the5avage/Q-Learning/

"Grokking Deep Reinforcement Learning" by Miguel Morales and "Deep Reinforcement Learning in Action" by Alexander Zai and Brandon Brown both look promising, though the code might be outdated. Looks like they use the OpenAI Gym environment, which has since been forked and maintained as Gymnasium.

https://course.fast.ai/

"You’ll see that fast.ai’s way of teaching is very different to what you might be used to, if you did a technical degree at university. Nearly all technical subjects at university are taught “bottom up”: start with basic foundations, and gradually work up to complete useful solutions to real world problems. But we go “top down”: start with complete useful solutions to real world problems, and gradually work down to the basic foundations. Education experts recommend this approach for more effective learning."

I don't think this discusses reinforcement learning (not sure about the comment you responded to). To my recollection, the first course goes through basic architectures and concepts, and the second discusses autoenvoders.

Yeah, the Fast.ai course does not discuss RL. The first part focuses on DL and the second part focuses on Diffusion based models

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