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Understanding Machine Learning: From Theory to Algorithms
21–30 of 56 posts
Re: Understanding Machine Learning: From Theory to Algorithms
#22Anyone who wants to demystify ML should read: The StatQuest Illustrated Guide to Machine Learning [0] By Josh Starmer. To this day I haven't found a teacher who could express complex ideas as clearly and concisely as Starmer does. It's written in an almost children's book like format that is very easy to read and understand. He also just published a book on NN that is just as good. Highly recommend even if you are al…
I have it in my bookshelf! I bought it on a whim, used, along with other CS books, but didn't think it's that good! I will try reading it. Thanks.
Re: Understanding Machine Learning: From Theory to Algorithms
#23Anyone who wants to demystify ML should read: The StatQuest Illustrated Guide to Machine Learning [0] By Josh Starmer. To this day I haven't found a teacher who could express complex ideas as clearly and concisely as Starmer does. It's written in an almost children's book like format that is very easy to read and understand. He also just published a book on NN that is just as good. Highly recommend even if you are al…
I would've thought that NN and ML would be taught together. Does he assume with the NN book that you already have a certain level of ML understanding?
Re: Understanding Machine Learning: From Theory to Algorithms
#24Earlier quoted context omitted.
I haven't read that book, but I can personally attest to Josh Starmer's StatQuest Youtube channel[1] being awesome! I used his lessons as a supplement to my studies when I was studying statistics in uni. [1]: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw
This is the 2nd or 3rd time in the last few weeks I've seen this person recommended. Must be something to that.
He’s great. I learned a ton from him when I was starting my computational biology studies as a grad student.
Re: Understanding Machine Learning: From Theory to Algorithms
#25Anyone who wants to demystify ML should read: The StatQuest Illustrated Guide to Machine Learning [0] By Josh Starmer. To this day I haven't found a teacher who could express complex ideas as clearly and concisely as Starmer does. It's written in an almost children's book like format that is very easy to read and understand. He also just published a book on NN that is just as good. Highly recommend even if you are al…
I would've thought that NN and ML would be taught together. Does he assume with the NN book that you already have a certain level of ML understanding?
Most ML is disjoint from the current NN trends, IMO. Compare Bishop's PRML to his Deep Learning textbook. First couple chapters are copy+paste preliminaries (probability, statistics, Gaussians, other maths background), and then they completely diverge. I'm not sure how useful classical ML is for understanding NNs.
Re: Understanding Machine Learning: From Theory to Algorithms
#26Anyone who wants to demystify ML should read: The StatQuest Illustrated Guide to Machine Learning [0] By Josh Starmer. To this day I haven't found a teacher who could express complex ideas as clearly and concisely as Starmer does. It's written in an almost children's book like format that is very easy to read and understand. He also just published a book on NN that is just as good. Highly recommend even if you are al…
Thanks for the recommendation. Purchased both them!
Re: Understanding Machine Learning: From Theory to Algorithms
#27I would recommend https://udlbook.github.io/udlbook/ instead if you're looking to learn about modern generative AI.
Thanks for the recommendation. Have you looked at Bishop’s Deep learning book ( https://www.bishopbook.com/ )? How would you compare both? Thanks again
You'll be happy with either. Bishop's approach is historically more mathematical (cf his 2006 PRML text), and you see that in the preliminaries chapters of Deep Learning, but there's less of this as the book goes on.
I've read chapters from both. Much overlaps, but sometimes one book or the other explains a concept better or provides different perspectives or details.
Re: Understanding Machine Learning: From Theory to Algorithms
#28Re: Understanding Machine Learning: From Theory to Algorithms
#29Earlier quoted context omitted.
I would've thought that NN and ML would be taught together. Does he assume with the NN book that you already have a certain level of ML understanding?
Most ML is disjoint from the current NN trends, IMO. Compare Bishop's PRML to his Deep Learning textbook. First couple chapters are copy+paste preliminaries (probability, statistics, Gaussians, other maths background), and then they completely diverge. I'm not sure how useful classical ML is for understanding NNs.
That's fair. My understanding is that NN and ML are similar insofar as they are both about minimizing a loss value (like negative log likelihood). And then the methods of doing that are very different and once you get even more advanced, NN concepts feel like a completely different universe.
Re: Understanding Machine Learning: From Theory to Algorithms
#30Earlier quoted context omitted.
I haven't read that book, but I can personally attest to Josh Starmer's StatQuest Youtube channel[1] being awesome! I used his lessons as a supplement to my studies when I was studying statistics in uni. [1]: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw
This is the 2nd or 3rd time in the last few weeks I've seen this person recommended. Must be something to that.
I thought I was having some deja vu. I had to keep checking the timestamps. The same person recommended the book 18 days ago: https://news.ycombinator.com/item?id=43390896 and then a different person also recommended the author's YouTube channel. I also bought the book since then. I guess I better get started reading it now. :)