> AI by Hand is the research publication of By Hand Research, founded by Prof. Tom Yeh. By Hand Research studies model interpretability and explainability at the math and algorithm level. Subscribers receive free new articles and join live seminars. Members get access to our full research library.
i created something similar a while back. Inspired by micrograd for showing the connection between math/calculus and code, then building along the way to a full NumPy deep learning library that I pretrained GPT-2 124M model with it. One way to learn is to trace through the PRs merged to the repo in chronological order.
Similarly, I’ve been working through Raschka’s Build a Large Language Model (From Scratch) https://sebastianraschka.com/llms-from-scratch/ and it’s been well worth it (particularly paired with his explainer videos)
It started mostly as an exercise to make sure I actually understood forward propagation and backpropagation rather than just using an existing framework.
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[ 0.27 ms ] story [ 4.1 ms ] thread> AI by Hand is the research publication of By Hand Research, founded by Prof. Tom Yeh. By Hand Research studies model interpretability and explainability at the math and algorithm level. Subscribers receive free new articles and join live seminars. Members get access to our full research library.
The philosophy is the same: "What I cannot create, I do not understand." https://github.com/workofart/ml-by-hand
Train your own LLM - https://github.com/angelos-p/llm-from-scratch
HN Discussion - https://news.ycombinator.com/item?id=48017948
https://nostarch.com/deep-learning-visual-approach
- other half have no visualizations whatsoever
- i have posted far better resources than this in the past collecting everything HN has over a 10 yr period on these subjects https://news.ycombinator.com/item?id=49183198
If anyone is interested: https://github.com/kvladsrc/gym/tree/main/neural_network/nn_...