Python framework for interpretable protein prediction
GitHub repository with 85 stars and 5 forks.
Language: Jupyter Notebook
Topics: explainability, feature-engineering, feature-selection, intepretable-machine-learning, intrepretability, machine-learning, positive-unlabeled-learning, protein-prediction
2026-06-05: 85 stars and 5 forks.
A game theoretic approach to explain the output of any machine learning model.
GitHub repository with 25,503 stars and 3,726 forks.
Trending score: 0.56; stars gained: +2; forks gained: +0.
Language: Jupyter Notebook
Topics: interpretability, machine-learning, deep-learning, gradient-boosting, shap, shapley
NVIDIA Cosmos is an open platform of world models, datasets, and tools that enables developers to build Physical AI for robots, autonomous vehicles, smart infrastructure, and more.
GitHub repository with 9,194 stars and 588 forks.
Trending score: 2.37; stars gained: +326; forks gained: +20.
Language: Jupyter Notebook
Sample code and notebooks for Generative AI on Google Cloud, with Gemini Enterprise Agent Platform
GitHub repository with 16,981 stars and 4,249 forks.
Trending score: 1.87; stars gained: +8; forks gained: +6.
Language: Jupyter Notebook
Topics: generative-ai, llm, vertex-ai, langchain, gemini, gemini-api
LLM Zoomcamp - a free online course about real-life applications of LLMs. In 10 weeks you will learn how to build an AI system that answers questions about your knowledge base.
GitHub repository with 5,614 stars and 1,015 forks.
Trending score: 1.87; stars gained: +93; forks gained: +13.
Language: Jupyter Notebook
GitHub repository with 2,685 stars and 332 forks.
Trending score: 1.82; stars gained: +48; forks gained: +12.
Language: Jupyter Notebook
Build LLM agents and multi-agent systems from scratch, with MCP, Skills, and A2A
GitHub repository with 131 stars and 45 forks.
Trending score: 1.70; stars gained: +32; forks gained: +14.
Language: Jupyter Notebook
Examples and guides for using the OpenAI API
GitHub repository with 73,988 stars and 12,525 forks.
Trending score: 1.57; stars gained: +24; forks gained: +12.
Language: Jupyter Notebook
Topics: openai, chatgpt, gpt-4, openai-api
Interactive architecture diagrams for codebases
GitHub repository with 2,091 stars and 177 forks.
Trending score: 1.26; stars gained: +20; forks gained: +1.
Language: Python
Topics: agents, aiagentic, explainability, llms, static-analysis, codedocs
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
GitHub repository with 20,597 stars and 2,576 forks.
Trending score: 0.84; stars gained: +5; forks gained: +1.
Topics: machine-learning, mlops, interpretability, explainability, responsible-ai, deep-learning
A game theoretic approach to explain the output of any machine learning model.
GitHub repository with 25,503 stars and 3,726 forks.
Trending score: 0.56; stars gained: +2; forks gained: +0.
Language: Jupyter Notebook
Topics: interpretability, machine-learning, deep-learning, gradient-boosting, shap, shapley
Fit interpretable models. Explain blackbox machine learning.
GitHub repository with 6,867 stars and 784 forks.
Trending score: 0.03; stars gained: +0; forks gained: +0.
Language: C++
Topics: ai, artificial-intelligence, bias, blackbox, differential-privacy, explainability
The flowchart pattern for backend code — self-explainable systems that AI can reason about. Causal traces, auto-generated tool descriptions, transactional state.
GitHub repository with 8 stars and 2 forks.
Trending score: 0.02; stars gained: +0; forks gained: +0.
Language: TypeScript
Topics: ai, ai-tools, backend, causal-trace, debugging, dfs