Stars
A technical report on convolution arithmetic in the context of deep learning
Statistical Rethinking: A Bayesian Course Using Python and NumPyro
Statistical Rethinking (2nd ed.) with NumPyro
Oryx is a library for probabilistic programming and deep learning built on top of Jax.
Library for reading and processing ML training data.
Paper Lists for Graph Neural Networks
Flax is a neural network library for JAX that is designed for flexibility.
Understanding Deep Learning - Simon J.D. Prince
My solutions to DLFC - Deep Learning: Foundations and Concepts
Notebooks about Bayesian methods for machine learning
Probabilistic Deep Learning finds its application in autonomous vehicles and medical diagnoses. This is an increasingly important area of deep learning that aims to quantify the noise and uncertain…
Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's method.
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphic…
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
A collection of literature after or concurrent with Masked Autoencoder (MAE) (Kaiming He el al.).
PyTorch implementation of normalizing flow models
Implementation of papers in 100 lines of code.
MADE (Masked Autoencoder Density Estimation) implementation in PyTorch
Probabilistic reasoning and statistical analysis in TensorFlow
A Python implementation of global optimization with gaussian processes.
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023
Pytorch implementations of density estimation algorithms: BNAF, Glow, MAF, RealNVP, planar flows
All the handwritten notes 📝 and source code files 🖥️ used in my YouTube Videos on Machine Learning & Simulation (https://www.youtube.com/channel/UCh0P7KwJhuQ4vrzc3IRuw4Q)
STAT 453: Intro to Deep Learning @ UW-Madison (Spring 2020)
Paper re-implementations from the course METU CENG796 Deep Generative Models.
😎 Awesome lists about all kinds of interesting topics
JAX - A curated list of resources https://github.com/google/jax