Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023
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Updated
Jun 10, 2024 - Jupyter Notebook
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.
JAX implementations of various deep reinforcement learning algorithms.
JAX/Flax implementation of finite-size scaling
An implementation of adan optimizer for optax
JAX implementation of Classical and Quantum Algorithms for Orthogonal Neural Networks by (Kerenidis et al., 2021)
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.
A Simplistic trainer for Flax
Goal-conditioned reinforcement learning like 🔥
Variational Graph Autoencoder implemented using Jax & Jraph
Implementation of PSGD optimizer in JAX
Direct port of TD3_BC to JAX using Haiku and optax.
The (unofficial) vanilla version of WaveRNN
dm-haiku implementation of hyperbolic neural networks
An Optax-based JAX implementation of the IVON optimizer for large-scale VI training of NNs (ICML'24 spotlight)
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