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.
AI foundations for software engineers.
| Date | Stars |
|---|---|
| 2026-07-30 | 1353 |
| 2026-08-10 | 1359 |
| 2026-08-18 | 1361 |
| 2026-08-20 | 1362 |
| 2026-08-25 | 1367 |
| 2026-09-01 | 1367 |
| 2026-09-05 | 1368 |
| 2026-09-06 | 1368 |
| 2026-09-08 | 1369 |
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| 2026-09-11 | 1369 |
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| 2026-09-15 | 1369 |
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| 2026-09-19 | 1370 |
| 2026-09-25 | 1369 |
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| 2026-10-05 | 1373 |
| 2026-10-10 | 1373 |
| 2026-10-12 | 1374 |
| 2026-10-29 | 1375 |
| 2026-10-30 | 1376 |
| 2026-11-04 | 1376 |
Today
— stars today
This week
+2 stars this week
This month
+3 stars this month
Momentum
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growth rate 0.15%/day
 # AI Foundations for Software Engineers This repo contains the resources software engineers need to build a foundational understanding of AI. It's split into four sections: 1. [General Resources](#general-resources) 2. [ML Foundations](#ml-foundations) 3. [LLMs](#llms) 4. [Engineering](#engineering) This guide assumes the reader has prior experience with programming. Feel free to skip around between resources as you see fit. I share more great resources as I find them on [X](https://x.com/loganthorneloe) and [Substack](substack.com/@loganthorneloe). Subscribe to my newsletter, [**AI for Software Engineers**](https://aiforswes.com/subscribe), for more AI/ML engineering resources. Enjoy! 🚀 ## General Resources General resources for software design and technical judgment that help when engineering AI systems. Consider this *optional*. - **[The Art of Doing Science and Engineering: Learning to Learn](https://press.stripe.com/the-art-of-doing-science-and-engineering)** by Richard W. Hamming Two of the most important things to understand as an engineer working in AI are that AI is fundamentally a research discipline and that continued learning matters as much in AI as it does in software engineering. This book helps readers understand both. There is also a [shorter essay version](https://www.cs.utexas.edu/~dahlin/bookshelf/hamming.html) of this you can read if you prefer. ## ML Foundations Resources for understanding the foundations of machine learning from the bottom up. - **[Hands-On Machine Learning with Scikit-Learn and PyTorch](https://www.oreilly.com/library/view/hands-on-machine-learning/9798341607972/)** by Aurélien Géron This book is the gold standard for getting hands-on with machine learning. You'll build a machine learning project end-to-end and get hands-on experience with the most important technologies and techniques in AI. It goes into great depth and covers many important topics in a single resource. The [accompanying repo](https://github.com/ageron/handson-mlp) includes notebooks and exercise solutions. - **[Mathematics of Machine Learning](https://www.packtpub.com/en-us/product/mathematics-of-machine-learning-9781837027873)** by Tivadar Danka This book teaches the linear algebra, probability, calculus, and optimization foundations that make models easier to reason about. This is another great resource that covers everything you need to know about ML math in a single book. Most engineers skip this part, but as AI advances, an understanding of the math behind it becomes more important. The book has an [accompanying repo](https://github.com/cosmic-cortex/mathematics-of-machine-learning-book). ## LLMs Resources for understanding large language models, the most important topic in modern AI. - **[Neural Networks & Transformer Series](https://www.3blue1brown.com/topics/neural-networks)** by 3Blue1Brown The transformer architecture is the most important concept to grasp when learning how LLMs work. This series builds a clear, deep visual intuition for how attention and neural networks function. - **[Build a Large Language Model (From Scratch)](https://www.manning.com/books/build-a-large-language-model-from-scratch)** by Sebastian Raschka This book walks through tokenization, embeddings, attention, transformer blocks, training, and fine-tuning so the pieces of an LLM become concrete. It takes you from zero to training your own LLM. The [accompanying repo](https://github.com/rasbt/LLMs-from-scratch) contains the code and exercises. - **[Build a Reasoning Model (From Scratch)](https://www.manning.com/books/build-a-reasoning-model-from-scratch)** by Sebastian Raschka Reasoning models have become fundamental to real-world AI applications. This book builds intuition for reasoning models by walking through the pieces needed to create one yourself and showing how the surrounding data, training process, and evaluation loop shape the beh
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Claude · @anthropics
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d14097662a027660, topic:roadmap, readme:roadmap
matched fp:d14097662a027660, topic:deep-learning