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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
The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.
Python port for algorithms shown in "Algorithms for Optimization"
Practical assignments of the Deep|Bayes summer school 2019
My collection of R Markdown templates, as an R package.
Course notes for CS228: Probabilistic Graphical Models.
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
Awesome resources on normalizing flows.
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
A collection of various deep learning architectures, models, and tips