Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
It is said that, Ilya Sutskever gave John Carmack this reading list of ~ 30 research papers on deep learning.
| Date | Stars |
|---|---|
| 2026-07-31 | 1725 |
| 2026-08-02 | 1726 |
| 2026-08-06 | 1753 |
Today
+27 stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Deep learning reading list from Ilya Sutskever > 深度学习精炼秘笈 > til, Ilya sutskever gave john carmack this reading list of approx 30 research papers and said, ‘If you really learn all of these, you’ll know 90% of what matters today.’ <br> [[Twitter Post]](https://twitter.com/keshavchan/status/1787861946173186062) [[Arc.net Link]](https://arc.net/folder/D0472A20-9C20-4D3F-B145-D2865C0A9FEE) <br> ## - **The Annotated Transformer.** Sasha Rush, et al. [[Blog]](https://nlp.seas.harvard.edu/annotated-transformer/) [[Code]](https://github.com/harvardnlp/annotated-transformer/) - **The First Law of Complexodynamics.** Scott Aaronson. [[Blog]](https://scottaaronson.blog/?p=762) - **The Unreasonable Effectiveness of Recurrent Neural Networks.** Andrej Karpathy. [[Blog]](https://karpathy.github.io/2015/05/21/rnn-effectiveness/) [[Code]](https://github.com/karpathy/char-rnn) - **Understanding LSTM Networks.** Christopher Olah. [[Blog]](https://colah.github.io/posts/2015-08-Understanding-LSTMs/) - **Recurrent Neural Network Regularization.** Wojciech Zaremba, et al. [[ArXiv]](https://arxiv.org/abs/1409.2329) [[pdf]](https://arxiv.org/pdf/1409.2329) [[Code]](https://github.com/wojzaremba/lstm) - **Keeping Neural Networks Simple by Minimizing the Description Length of the Weights.** Geoffrey E. Hinton and Drew van Camp. [[Paper]](https://dl.acm.org/doi/10.1145/168304.168306) [[pdf]](https://www.cs.toronto.edu/~hinton/absps/colt93.pdf) - **Pointer Networks.** Oriol Vinyals, et al. [[Paper]](https://papers.nips.cc/paper/5866-pointer-networks) [[pdf]](https://arxiv.org/pdf/1506.03134) - **ImageNet Classification with Deep Convolutional Neural Networks.** Alex Krizhevsky, et al. [[Paper]](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) [[pdf]](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) - **Order Matters: Sequence to sequence for sets.** Oriol Vinyals, et al. [[ArXiv]](https://arxiv.org/abs/1511.06391) [[pdf]](https://arxiv.org/pdf/1511.06391) - **GPipe: Easy Scaling with Micro-Batch Pipeline Parallelism.** Yanping Huang, et al. [[ArXiv]](https://arxiv.org/abs/1811.06965) [[pdf]](https://arxiv.org/pdf/1811.06965) - **Deep Residual Learning for Image Recognition.** Kaiming He, et al. - **Multi-Scale Context Aggregation by Dilated Convolutions.** Fisher Yu and Vladlen Koltun. - **Neural Message Passing for Quantum Chemistry.** Justin Gilmer, et al. - **Attention Is All You Need.** Ashish Vaswani, et al. - **Neural Machine Translation by Jointly Learning to Align and Translate.** Dzmitry Bahdanau, et al. - **Identity Mappings in Deep Residual Networks.** Kaiming He, et al. - **A simple neural network module for relational reasoning.** Adam Santoro, et al. - **Variational Lossy Autoencoder.** Xi Chen, et al. - **Relational recurrent neural networks.** Adam Santoro, et al. - **Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton.** Scott Aaronson, et al. - **Neural Turing Machines.** Alex Graves, et al. - **Deep Speech 2: End-to-End Speech Recognition in English and Mandarin.** Dario Amodei, et al. - **Scaling Laws for Neural Language Models.** Jared Kaplan, et al. - **A Tutorial Introduction to the Minimum Description Length Principle.** Peter Grunwald. - **Machine Super Intelligence.** Shane Legg. - **Kolmogorov Complexity and Algorithmic Randomness.** A.Shen, V. A. Uspensky, and N. Vereshchagin. - **CS231n: Convolutional Neural Networks for Visual Recognition.**
Excerpt of 3,549 characters
Read on GitHub
3
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b70e3ff12bf9d25f, desc:reading list