TensorLy: Tensor Learning in Python.

A toolkit for developing and comparing reinforcement learning algorithms.

A large-scale dataset of both raw MRI measurements and clinical MRI images.

Detailed Installation procedure of CUDA, cuDNN, OpenCv and PyTorch for Machine and Deep Learning Tasks

Sharing Deep Learning Research Models with Lightning Part 1: Building A Super Resolution App

Scalable Image Analysis and Shape Modelling

A library of extension and helper modules for Python's data analysis and machine learning libraries.

Code for "TorchMetrics -- How do we use it, and what's the difference between .update() and .forward()"

Model evaluation utilities

An IPython magic extension for printing date and time stamps, version numbers, and hardware information

A web based tool for visualization of the forward and reverse modes of automatic differentiation

A collection of various deep learning architectures, models, and tips

A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)

STAT 453: Intro to Deep Learning @ UW-Madison (Spring 2021)

Volume Rendering Sample project using C++, Qt5, OpenGL, OpenCL

Framework for building statistical image and shape models

A collection of experiments with volume rendering packages.

Volume renderer made with Qt5, C++ and OpenGL

A MATLAB / MEX / CUDA-MEX implementation of Convolutional Restricted Boltzmann Machines.

This project is aimed at jump-starting a C/C++ project that can build libraries, binaries and have a working unit test suite. It uses CMake build system and is deliberately completely minimal.

Columbia Image and Face Search tool for MEMEX

Diagrams describing Apache Hadoop internals (2.3.0 or later).

Convolutional RBM implemented with Pytorch

code for paper "Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis"

Code for paper "Distilling Intractable Generative Models".

Summary of Machine Learning: A Probabilistic Perspective book

Implementation of some fundamental Machine Learning algorithms completely written in Pytorch

Code for reproducing key results in the paper "Improving Variational Inference with Inverse Autoregressive Flow"