Stars
🔥 Turn entire websites into LLM-ready markdown or structured data. Scrape, crawl and extract with a single API.
Code files for advanced LLM Course
🔮 Instill Core is a full-stack AI infrastructure tool for data, model and pipeline orchestration, designed to streamline every aspect of building versatile AI-first applications
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
A set of LangChain Tutorials from my youtube channel
<⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.
PyGWalker: Turn your pandas dataframe into an interactive UI for visual analysis
Chat with your database (SQL, CSV, pandas, polars, mongodb, noSQL, etc). PandasAI makes data analysis conversational using LLMs (GPT 3.5 / 4, Anthropic, VertexAI) and RAG.
Model interpretability and understanding for PyTorch
Perform data science on data that remains in someone else's server
Curated list of open source tooling for data-centric AI on unstructured data.
[AAAI 2024] Mab2Rec: Multi-Armed Bandits Recommender
A collection of prompts for use with GPT-4 via ChatGPT, OpenAI API w/ Gradio frontend & notebook
A collection of libraries to optimise AI model performances
Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning
A collection of various deep learning architectures, models, and tips
A Python implementation of LightFM, a hybrid recommendation algorithm.
Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
A Python library for calculating a large variety of metrics from text
Collection of useful data science topics along with articles, videos, and code
Dataframes powered by a multithreaded, vectorized query engine, written in Rust
A library for debugging/inspecting machine learning classifiers and explaining their predictions
Algorithms for explaining machine learning models
Fit interpretable models. Explain blackbox machine learning.
moDel Agnostic Language for Exploration and eXplanation
Code for the TCAV ML interpretability project