You wanna get into how ML works beneath the frameworks:

• “Understanding Deep Learning” by Simon J. D. Prince
• “The Hundred Pages Machine Learning Book” by Andriy Burkov
• “The Little Book of Deep Learning” by François Fleuret

You lack the maths skills to read the above:

• “Mathematics for Machine Learning” by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
• “Linear Algebra Done Right” by Sheldon Axler
• “Linear Algebra” by 3Blue1Brown

You just wanna do ML:

• FastAI Book
• FastAI video course

You want to learn about LLMs

• Karpathy’s youtube channel – this has the most amount of signal, highest amount of alpha to learn about LLMs and building out GPT-2 level transformer models

Tips

• Do the exercises, always
• You’re not learning math if you don’t do the exercises
• You’re not learning ML if you don’t build models! Train them from scratch! Implement a multi-layer percepetron from scratch!
• Tinker after chapters/videos, tweak the examples or exercises, build simple models, build yet another MNIST classifier, but tinker!