From 3Blue1Brown.

Breaking down how Large Language Models work
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Here are a few other relevant resources

Build a GPT from scratch, by Andrej Karpathy

If you want a conceptual understanding of language models from the ground up, @vcubingx just started a short series of videos on the topic:

If you’re interested in the herculean task of interpreting what these large networks might actually be doing, the Transformer Circuits posts by Anthropic are great. In particular, it was only after reading one of these that I started thinking of the combination of the value and output matrices as being a combined low-rank map from the embedding space to itself, which, at least in my mind, made things much clearer than other sources.
https://transformer-circuits.pub/2021/framework/index.html

Site with exercises related to ML programming and GPTs
https://www.gptandchill.ai/codingproblems

History of language models by Brit Cruise, @ArtOfTheProblem

An early paper on how directions in embedding spaces have meaning:
https://arxiv.org/pdf/1301.3781.pdf

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Timestamps

0:00 – Predict, sample, repeat
3:03 – Inside a transformer
6:36 – Chapter layout
7:20 – The premise of Deep Learning
12:27 – Word embeddings
18:25 – Embeddings beyond words
20:22 – Unembedding
22:22 – Softmax with temperature
26:03 – Up next