Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023

The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.

Accelerate, Optimize performance with streamlined training and serving options with JAX.

This is the official repository for the paper "Flora: Low-Rank Adapters Are Secretly Gradient Compressors" in ICML 2024.

Experiment with diffusion models that you can run on your local jupyter instances

Simple CIFAR10 ResNet example with JAX.

JAX implementations of various deep reinforcement learning algorithms.

Implementation of PSGD optimizer in JAX

Maximal Update Parametrization (μP) with Flax & Optax.

JAX/Flax implementation of finite-size scaling

Goal-conditioned reinforcement learning like 🔥

An implementation of adan optimizer for optax

H-Former is a VAE for generating in-between fonts (or combining fonts). Its encoder uses a Point net and transformer to compute a code vector of glyph. Its decoder is composed of multiple independent decoders which act on a code vector to reconstruct a point cloud representing a glpyh.

JAX implementation of Classical and Quantum Algorithms for Orthogonal Neural Networks by (Kerenidis et al., 2021)

Variational Graph Autoencoder implemented using Jax & Jraph

A Simplistic trainer for Flax

An Optax-based JAX implementation of the IVON optimizer for large-scale VI training of NNs (ICML'24 spotlight)

Direct port of TD3_BC to JAX using Haiku and optax.

Sophia optimizer with ScheduleFree

dm-haiku implementation of hyperbolic neural networks