Reproducing 30 AI Papers Using Pure NumPy
udmrzn · x · 2026-07-16
An open-source project named Sutskever 30 has implemented 30 foundational papers mentioned by Ilya Sutskever using pure NumPy, and is marked as 100% complete.
Project highlights include:
- Zero dependency on deep learning frameworks; everything is built from scratch using only NumPy.
- Companion Jupyter Notebooks for interactive learning and code modification.
- Built-in synthetic data, allowing users to run the code immediately without downloading external datasets.
- Coverage of core concepts including RNN, LSTM, Transformer, ResNet, VAE, RAG, and Scaling Laws.
The author notes that these notebooks are perfect for those wanting to deeply understand the underlying principles of AI, taking learners all the way from character-level RNNs to more general AI theories.
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