Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models.

🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…

The simplest, fastest repository for training/finetuning medium-sized GPTs.

Making large AI models cheaper, faster and more accessible

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.

A list of all named GANs!

Python package built to ease deep learning on graph, on top of existing DL frameworks.

An educational resource to help anyone learn deep reinforcement learning.

Declarative visualization library for Python

PyTorch3D is FAIR's library of reusable components for deep learning with 3D data

Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)

Deep Agents is an agent harness built on langchain and langgraph. Deep Agents are equipped with a planning tool, a filesystem backend, and the ability to spawn subagents - making them well-equipped…

Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch

A PyTorch implementation of EfficientNet

Uniform Manifold Approximation and Projection

A Collection of Variational Autoencoders (VAE) in PyTorch.

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

Build Graph Nets in Tensorflow

Image augmentation library in Python for machine learning.

High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.

Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.

A collection of loss functions for medical image segmentation

Differentiable architecture search for convolutional and recurrent networks

A highly efficient implementation of Gaussian Processes in PyTorch

Fast, flexible and easy to use probabilistic modelling in Python.

Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2

TensorFlow Reinforcement Learning

Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)