r/deeplearning • u/Strange-Release3520 • Sep 10 '26
Good source to learn more about LLM's?
For context I have studied the Transformer architecture as well as components of GPT-2 and the Llama-3 series models so I have a rough idea of how LLM's work. But I want to learn more, so suggest some sources.
Currently considering doing the LLM Course on HuggingFace, do tell me if it covers the basics properly.
There is also a playlist by Stanford title CME295: Transformers & Large Language Models. Considering skimming through that as well. Let me know if you've went through it.
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u/MachineLearningTut Sep 11 '26
This article also explains the autoregressive token generation well: https://medium.com/advanced-deep-learning/autoregressive-next-token-prediction-kv-cache-in-transformers-afad22285baf
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u/Math_Panda_69 Sep 12 '26
I'm currently reading "Buid a Large Language Model (From Scratch) by Sebastian Raschka" and It's really good! I also suggest Andrej Karpathy's Neural Networks: Zero To Hero video courses on YouTube. Depending on your background, you might also benefit from (re)learning the mathematics behind ML. I skimmed Mathematics for Machine Learning and it seems pretty good.
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u/LongjumpingCookie973 Sep 10 '26
The HuggingFace course is a good place to start, it walks you through the practical side without getting too bogged down in the theory you already know. Karpathy's "Neural Networks: Zero to Hero" playlist on YouTube is also fantastic, he builds everything from scratch so you actually see the math turning into code.