Comment by TechDebtDevin
15 days ago
Anyone 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 already an expert as it will give you great ways to teach and communicate complex ideas in ML.
[0]: https://www.goodreads.com/book/show/75622146-the-statquest-i...
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. :)
2 replies →
He’s great. I learned a ton from him when I was starting my computational biology studies as a grad student.
[dead]
I love StatsQuest and own this book which I like a lot. However I could not recommend it as a way of ML beyond a surface level. It's a little bit outdated although his NN book, which I've not read, may remedy this.
I'm just curious for folks who have read through the material OP suggested as well as, the book linked in this HN thread, are your guys primary motivation to understand and fill in that curiosity part of your head vs making a career out of this?
Is it reasonable to think that if one grinds to the book suggested here and background in web/dev SWE, one can break into ML/AI role?
Most ML/AI roles have requirements for a strong mathematical background (at least what I have seen in germany).
If you can show off some skills I still wouldnt completely rule it out. Reading a single book cover to cover wont cut it though imo.
If you have an undergraduate’s understanding of calculus and linear algebra, you’re as or more advanced than the legion of ML PhD candidates I see graduating all the time. A field like that is running on hype, and has no quality control at all. I’ve seen people get hired into Ivy League tenure track jobs without knowing how linear algebra really works.
1 reply →
I haven‘t yet read the book but his Youtube channel is always my first go-to place for ideas on how to communicate these concepts easily. My work involves using ML in econometric analyses and most economists do not intuitively understand ML.
There's also Math Academy's Math for Machine Learning.
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.
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.
Thanks for the recommendation. Purchased both them!
Didn’t know he had written a book. His YouTube channel is awesome.