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Comment by DanaNewton | Hacker News Reader

I found this list useful, and I appreciated the context and alternatives given to the standard recommendations. However, this comment epitomizes what drives me nuts about HN, negativity, with no feedback or justification.

What, if anything is wrong with the list of links provided?

What would you have recommended? And why does this list come up short?

What led you to believe the link would be more than "just" a list of learning classes?

You take issue with the fact this list was compiled by someone getting their master's in Deep Learning, why? If it matters who gives the suggestions, might I ask who you are and what qualifications you posses to so flippantly discount someone's contribution?

> proves that there's a massive hip about Neural Network among programmers

Wouldn't the opposite be true, if people learned more about the subject ?!?!

> I am very sorry for the negativity

Are you?

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If you ask me about ML I'll say that starting with convolutional nets for images is starting on the wrong side (end instead of beginning). Andrew Ng course is better, but still somewhat lacks theory and general ML insights.

I'd recommend new Coursera Washington University ML course (https://class.coursera.org/machlearning-001/lecture) as a solid foundation. You'll learn key ML concepts (bias vs variance, bayes theory etc.) and the lector is simply the best I've seen.

Following that Ian Goodfellow book http://www.deeplearningbook.org/ is very good.

If you want to learn foundations of Reinforcement Learning I'd recommend Sutton & Barto https://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.h...

+1 to Sutton & Barto. There is a second edition of their book available; see a link at https://webdocs.cs.ualberta.ca/~sutton/book/the-book.html.

https://www.udacity.com/course/reinforcement-learning--ud600 is also good; it is not super-modern, but it is easily accessible, and it covers a large part of classical RL - this helps if you want to read recent RL papers because terminology and ideas become more familiar.

I'm not sure Andrey Karpaty's blog post (linked in the article) is a good intro to RL - it starts with Policy Gradient which is on a complex side of RL techniques spectrum. From other sources I've heard Policy Gradient is harder to get working on an arbitrary problem than e.g. Q-Learning, but don't quote me on that :)

If one doesn't want to dive deep in RL, and just want to start with recent methods then https://www.nervanasys.com/demystifying-deep-reinforcement-l... blog posts provide a very good starting point.

I think he's more upset with the fact that it doesn't quite live up to the title it holds, and is disappointed it's just a list of deep learning courses most people (who have bothered to look into learning about ml) know about already.

I also think you're taking this too personally.

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