A curated list of the most essential GitHub repos that cover the AI & ML landscape. If you like to add or update projects, feel free to open an issue or submit a pull request. Contributions are very welcome!
- Natural Language Processing (NLP)
- Large Language Models(LLM)
- Computer Vision
- Data Science
- Machine Learning
- Machine Learning Engineerings Operations (MLOps)
- Data Engineering
- nlp-tutorial: nlp-tutorial is a tutorial for who is studying NLP(Natural Language Processing) using Pytorch. Most NLP models were implemented with less than 100 lines of code.
- LLMs Practical Guide: The Practical Guides for Large Language Models
- LLM Survey: A collection of papers and resources related to Large Language Models
- Open LLMs: List of LLMs that are all licensed for commercial
- Awesome LLM: Curated list of papers about large language models, especially relating to ChatGPT
- Awesome Decentralized LLM: Collection of LLM resources that can be used to build products you can "own" or to perform reproducible research
- LangChain: Building applications with LLMs through composability
- Awesome LangChain: Curated list of tools and projects using LangChain
- Awesome Graph LLM: A collection of AWESOME things about Graph-Related Large Language Models (LLMs).
- OpenLLM: An open platform for operating large language models (LLMs) in production
- LLM Zoo: democratizing ChatGPT
- Awesome Computer Vision: A curated list of awesome computer vision resources
- Computer Vision Tutorials by Roboflow
- Transformer in Vision: paper list of some recent Transformer-based CV works
- Awesome-Referring-Image-Segmentation: A collection of referring image segmentation papers and datasets
- awesome-vision-language-pretraining-papers: Recent Advances in Vision and Language PreTrained Models (VL-PTMs)
- Awesome Vision-and-Language: A curated list of awesome vision and language resources,
- Awesome-Temporal-Action-Detection-Temporal-Action-Proposal-Generation
- Awesome-Referring-Image-Segmentation: A collection of referring image segmentation papers and datasets.
- Awesome Masked Autoencoders: A collection of literature after or concurrent with Masked Autoencoder (MAE)
- Awesome Visual-Transformer: Collection of some Transformer with Computer-Vision (CV) papers
- Transformer-Based Visual Segmentation: A Survey
- Awesome-Segmentation-With-Transformer
- CVPR 2o23 Paper with Code
- Awesome Deepfakes Detection
- Weekly-Top-Computer-Vision-Papers
- Data Science for Beginners - A Curriculum
- Data Science Resources
- freeCodeCamp.org's open-source codebase and curriculum
- List of Data Science/Big Data Resources
- Open Source Society University: Path to a free self-taught Education in Data Science
- AWESOME DATA SCIENCE: An open-source Data Science repository to learn and apply towards solving real-world problems.
- Data Science ALL CHEAT SHEET
- Data Science End-to-End Projects
- Data Analysis Projects
- Data Science Interview Resources
- Data-Science Interview Questions Answers
- Data-science-best-resources
- Amazing-Feature-Engineering
- Complete-Life-Cycle-of-a-Data-Science-Project
- Data Science Cheatsheet
- PandasAI
- GitHub Community Discussions: In this repository, you will find categories for various product areas. Feel free to share feedback, discuss topics with other community members, or ask questions.
- Awesome Machine Learning: A curated list of awesome machine learning frameworks, libraries and software (by language).
- Machine Learning & Deep Learning Tutorials: This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources
- Best-of Machine Learning with Python: A ranked list of awesome machine learning Python libraries.
- TensorFlow Examples: This tutorial was designed for easily diving into TensorFlow, through examples. For readability, it includes both notebooks and source codes with explanations, for both TF v1 & v2.
- Machine Learning Projects
- Randy Olson's data analysis and machine learning projects
- Minimum Viable Study Plan for Machine Learning Interviews
- Machine Learning Interview Questions: Machine Learning and Computer Vision Engineer
- Must Read Machine Learning & Deep Learning Papers
