sutskever-30-implementations
Small educational NumPy implementations, as Jupyter notebooks, of the 30 papers on Ilya Sutskever's reading list. Useful for learning core deep learning ideas by running simple code.
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Overview
This repository contains small educational implementations of the 30 papers on Ilya Sutskever's reading list, written as Jupyter notebooks. Each notebook uses only NumPy, with no deep learning framework, and generates its own synthetic data so it runs straight away. Visualizations and explanations accompany the code. The README reports all 30 papers as complete.
Key features
- One notebook per paper, covering all 30 papers
- Uses only NumPy, with no deep learning framework
- Synthetic data included, so notebooks run immediately
- Visualizations and explanations of each core idea
- Topics include RNNs, LSTMs, pruning and more
Best for
Learners who want to understand core deep learning ideas by running simple code instead of large frameworks. These are toy versions meant for teaching, not for production use.
- Upstream
- pageman/sutskever-30-implementations
- Fork on GitHub
- Guo-astro/sutskever-30-implementations
- Upstream stars
- 5.2k
- Category
- Learning, interviews and curated lists
- License
- No license declaredWithout a license, the author keeps all rights. Ask the upstream owner before reusing the code.
- Forked
- 2026-08-17
- Sync status
- In syncLast synced 2026-10-10
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