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.
A group of notebooks and other files which can help you learn AI from scratch.
| Date | Stars |
|---|---|
| 2026-07-24 | 1312 |
| 2026-07-25 | 1312 |
| 2026-07-28 | 1312 |
| 2026-07-30 | 1312 |
| 2026-08-06 | 1312 |
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growth rate 0.00%/day
# AI Engineering: Hands-on       <img width="2000" height="600" alt="image" src="https://github.com/user-attachments/assets/0c161d9a-3227-4dd7-a416-ad97bc9742a9" /> A complete, hands-on guide to becoming an AI Engineer. This repository is designed to help you learn AI from first principles, build real neural networks, and understand modern LLM systems end-to-end. You'll progress through math, PyTorch, deep learning, transformers, RAG, and OCR — with clean, intuitive Jupyter notebooks guiding you at every step. Whether you're a beginner or an engineer levelling up, this repo gives you the clarity, structure, and intuition needed to build real AI systems. #### ⭐ Star This Repo If you learn something useful, a star is appreciated. ## Repository Structure ### 1. Math Fundamentals - Math functions, derivatives, vectors, and gradients - Matrix operations and linear algebra - Probability and statistics ### 2. PyTorch Basics - Creating and manipulating tensors - Matrix multiplication, transposing, and reshaping - Indexing, slicing, and concatenating tensors - Special tensor creation functions ### 3. Neural-Network(NN) - Building neurons, layers, and networks from scratch - Normalization techniques (RMSNorm) - Activation functions - Optimizers (Adam, Muon) and learning rate decay ### 4. Transformers - Attention and self-attention mechanisms - Multi-head attention - Decoder-only transformer architecture ### 5. Retrieval-Augmented Generation (RAG) - Building RAG pipelines end to end - Indexing, retrieval, chunking strategies - Integrations with embedding models and vector stores - Cloud LLM support: [Atlas Cloud](https://www.atlascloud.ai/?utm_source=github&utm_medium=link&utm_campaign=ai-hands-on) (`deepseek-ai/DeepSeek-V3-0324` by default), [MiniMax](https://www.minimax.io/) (M3), OpenAI, or any OpenAI-compatible API for richer answer generation ### 6. Optical Character Recognition (OCR) - OCR pipeline and utilities - Preprocessing images and extracting text ## Books Recommended reading to deepen your understanding (not included): - `AI Engineering` by Chip Huyen - `Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow` by Aurélien Géron - `Deep Learning` by Ian Goodfellow, Yoshua Bengio, and Aaron Courville - `The Elements of Statistical Learning` by Trevor Hastie, Robert Tibshirani, and Jerome Friedman - `Neural Networks and Deep Learning` by Michael Nielsen - `SQL Cookbook` by Anthony Molinaro For more books in AI/ML, I have created another repo for this [Check Here](https://github.com/Ramakm/AI-ML-Book-References.git). I will be adding lot more in coming days/months. If you are interested to read book, go check this repo out. ## Learning Path For a recommended step-by-step progression through the materials, see the Learning Path: - `Start_here/learning_path.md` ## Requirements Install dependencies with: ```bash pip install -r requirements.txt ``` Some subfolders (for example `5.RAG/` and `6.OCR/`) include their own `requirements.txt` with additional dependencies. ## Usage Recommended workflow: 1. Open Jupyter in the project root: ```bash jupyter lab # or jupyter notebook ``` 2. Work through notebooks in order: - `1.Math/` - `2.PyTorch/` - `3.Neural-Network(NN)/` - `4.Transformer/` 3. Folder to run separately: - `5.RAG/` - `6.OCR/` 4. Resources 5. Basic ML Model Implementation (Supervised + Un-supervised + RL) - `1.Linear Regression` - `2.Logistic R
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3e14f6278eeea1a6, topic:neural-network, topic:pytorch
matched fp:3e14f6278eeea1a6, topic:ocr, readme:ocr, readme:chunking
matched fp:3e14f6278eeea1a6, topic:transformer, readme:transformer architecture
matched fp:3e14f6278eeea1a6, topic:rag, readme:retrieval-augmented generation, readme:retrieval augmented