The biggest challenge with ML models isn't the algorithm but the organization of contextual knowledge. In my experience, hierarchical structuring of documentation significantly improves results, especially when working with LLMs.
Understanding Machine Learning: From Theory to Algorithms
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Re: Understanding Machine Learning: From Theory to Algorithms
#42Anyone 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'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.
Re: Understanding Machine Learning: From Theory to Algorithms
#43Earlier quoted context omitted.
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. :)
Is there a chapter there on optimising sells through artificial activity? ;)
Re: Understanding Machine Learning: From Theory to Algorithms
#44I personally like Cosma Shalizi's book
Re: Understanding Machine Learning: From Theory to Algorithms
#45Why not from algorithm to theory ?
Re: Understanding Machine Learning: From Theory to Algorithms
#46I have read parts of it years ago. As far as I remember, this is very theoretical (lots of statistical learning theory, including some IMHO mistaken treatment of Vapnik's theory of structural risk minimization), with strong focus on theory and basicasically zero focus on applications. Which would be completely outdated by now anyway, as the book is from 2014, an eternity in AI. I don't think many people will want to…
Theory is still needed if you want to understand things like variational inference (which is in turn needed to understand things like diffusion models). It’s just like physics - you need math theories to understand things like quantum mechanics, because otherwise it might not make sense.
I think machine learning research is more like engineering, where you do need some math, but you don't need a physics degree. You don't need to understand everything first to discover that some engineering solutions work and others don't. And most abstract theories likely wouldn't have helped you anyway because they are not sufficiently concrete to apply to what you are doing in practice.
Re: Understanding Machine Learning: From Theory to Algorithms
#47Earlier quoted context omitted.
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.
Re: Understanding Machine Learning: From Theory to Algorithms
#48Earlier quoted context omitted.
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. :)
Not just recommending it again, but exactly the same comment.
Re: Understanding Machine Learning: From Theory to Algorithms
#49This is my favorite introductory machine learning theory (statistical learning theory) book, which is far more accessible than many others.
Re: Understanding Machine Learning: From Theory to Algorithms
#50Anyone 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 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.