Stars
What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?
Research code & papers from members of vx-underground.
General differential equation solver and bifurcation package
NXT-Python is a python driver/interface for the Lego Mindstorms NXT robot based on NXT_python.
TLC is a model checker for specifications written in TLA+. The TLA+Toolbox is an IDE for TLA+.
DO NOT CHECK OUT THESE FILES FROM GITHUB UNLESS YOU KNOW WHAT YOU ARE DOING. (See below.)
Code and other material for the book "Deep Learning and the Game of Go"
CodeQL: the libraries and queries that power security researchers around the world, as well as code scanning in GitHub Advanced Security
A powerful and user-friendly binary analysis platform!
Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.
Reverse proxy that inverts the direction of traffic
🦜🔗 Build context-aware reasoning applications
open source tools to generate mypy stubs from protobufs
A high level scripting API for bot builders, developers, and maintainers.
Notebooks, code samples, sample apps, and other resources that demonstrate how to use, develop and manage machine learning and generative AI workflows using Google Cloud Vertex AI.
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Code for "Learning Compositional Rules via Neural Program Synthesis"
Flax is a neural network library for JAX that is designed for flexibility.
Research language for array processing in the Haskell/ML family
ECMAScript parsing infrastructure for multipurpose analysis
A Python SDK for Vertex AI, a fully managed, end-to-end platform for data science and machine learning.
Tensorflow implementation of integrated gradients presented in "Axiomatic Attribution for Deep Networks". It explains connections between two tensors.
Python/Keras implementation of integrated gradients presented in "Axiomatic Attribution for Deep Networks" for explaining any model defined in Keras framework.