LLM basics
From "what is a neural network?" to "I get how ChatGPT works" in twelve steps. Do them in order; each one builds on the last.
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Tick steps off as you go. Your progress is saved in this browser only.
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But what is a neural network?
3Blue1Brown. The clearest picture of neurons, weights and layers you'll find. Pure intuition.
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#001 What a neural network actually is
Build one in a spreadsheet so the video's pictures turn into numbers you can poke.
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#004 Backprop by hand
How the network learns. One pencil, one tiny network, one chain rule.
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#007 Tokenizers are weirder than you think
Before a model reads text, text gets chopped into tokens. This explains a lot of odd LLM behavior.
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Transformers, the tech behind LLMs
3Blue1Brown. A visual tour of a transformer before you build one.
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#012 Attention, explained until it clicks
The core mechanism, in NumPy. Once this clicks, the rest is plumbing.
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The Illustrated Transformer
Jay Alammar. The same architecture again, drawn step by step. Repetition from a new angle helps it stick.
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Let's build GPT: from scratch, in code
Andrej Karpathy. The big one. Code along and you'll have trained a small GPT by the end.
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Attention Is All You Need
Read the original now. After steps 1–8 it reads like a summary of things you already know.
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Training language models to follow instructions with human feedback
Ouyang et al. (InstructGPT). How a text predictor becomes an assistant. Focus on sections 1–3.
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#002 KV cache: the reason chat feels fast
How models actually serve answers quickly. Inference is its own world.
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The Hugging Face LLM Course
Finish with the practical tools: load real models, fine-tune one, share it. You're a Hero now.