If I wanted to learn the fundamentals of ML so that I can better lead a team of ML engineers, what resources, blogs, or books would you recommend I check out ?
PS- I’m already a software engineering manager and I have no ML experience.
PS- I’m already a software engineering manager and I have no ML experience.
Comments
https://app.thestorygraph.com/books/ca8dac6e-4b49-4eb4-9ba4-571d398f6ce0
Unless it’s been updated in the last few years, it misses Transformers, but it has all the classic ML algorithms and all the theory.
https://www.coursera.org/collections/machine-learning
O’Reilly has a lot of books in this area
This was particularly good discussing the process of making and using LLM in companies using common terms. Some courses get really too deep into linear algebra for practical use. https://www.coursera.org/account/accomplishments/verify/X6Q05HJ3J0NB?utm_source=ios&utm_medium=certificate&utm_content=cert_image&utm_campaign=sharing_cta&utm_product=course
https://www.manning.com/books/build-a-large-language-model-from-scratch
For getting concepts, I like Statquest.
For coding it, I think you can pretty much just pick a concept and hack away at it, asking llms and stuff for questions, looking stuff up if it's beyond undergrad level
I manage a team that's been covering both; but the main things in common are that you need good eval + (closely related) to be really sure actual users will find the output valuable. The impl side is quite different (& many ML engineers dislike gen AI).
Actually, learning the tech enough to help guide good work is challenge one; challenge two is supporting learning & development — I've found this tough.
https://m.youtube.com/watch?v=aircAruvnKk
As starting point I would recommend two evergreens: hertz: Introduction To The Theory Of Neural Computation, Bishop:
Pattern Recognition and Machine Learning.
https://course.fast.ai/