Not a complete answer, but here are the most helpful resources for understanding transformer basics in particular: Original transformer paper: https://arxiv.or

Not a complete answer, but here are the most helpful resources for understanding transformer basics in particular: Original transformer paper: https://arxiv.org/abs/1706.03762 https://arxiv.org/abs/1706.03762 Illustrated transformer: http://jalammar.github.io/illustrated-transformer/ http://jalammar.github.io/illustrated-transformer/ Transformer visualization: https://bbycroft.net/llm https://bbycroft.net/llm minGPT (Karpathy): https://github.com/karpathy/minGPT https://github.com/karpathy/minGPT --- Next, some foundational textbooks for general ML and deep learning: Elements of Statistical Learning (aka the bible): https://hastie.su.domains/ElemStatLearn/ https://hastie.su.domains/ElemStatLearn/ Probabilistic ML: https://probml.github.io/pml-book/book2.html https://probml.github.io/pml-book/book2.html Deep Learning Book (Goodfellow/Bengio): https://www.deeplearningbook.org/ https://www.deeplearningbook.org/ Understanding Deep Learning: https://udlbook.github.io/udlbook/ https://udlbook.github.io/udlbook/ --- Finally, assorted tutorials/resources/intro courses: Beyond the Illustrated Transformer: https://news.ycombinator.com/item?id=35712334 https://news.ycombinator.com/item?id=35712334 AI Zero to Hero: https://karpathy.ai/zero-to-hero.html https://karpathy.ai/zero-to-hero.html AI Canon: https://a16z.com/2023/05/25/ai-canon/ https://a16z.com/2023/05/25/ai-canon/ LLM University by Cohere: https://llm.university/ https://llm.university/ Practical Guide to LLMs: https://github.com/Mooler0410/LLMsPracticalGuide https://github.com/Mooler0410/LLMsPracticalGuide Practical Deep Learning for Coders: https://course.fast.ai/Lessons/part2.html https://course.fast.ai/Lessons/part2.html --- Hope that helps!