Popular GitHub Repositories for Backprop

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.

Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.

Implementing Bayes by Backprop with PyTorch. Applied on time-series prediction.

A primer on Bayesian Neural Networks. The aim of this reading list is to facilitate the entry of new researchers into the field of Bayesian Deep Learning, by providing an overview of key papers. More details: "A Primer on Bayesian Neural Networks: Review and Debates"

Bayesian deep learning for remaining useful life estimation via Stein variational gradient descent

Lightweight Bayesian deep learning library for fast prototyping based on PyTorch

This repository contains all my theroy reports, written assignments and programming code that I wrote/referrd for the DL course at IIT,Madras taught my advisor Prof.Mitesh Khapra.

Comparison of a network implemented via Variational Inference with the same network implemented via Monte Carlo Dropout

A Lesson and an interactive Tool for learning Artificial Neural Networks. (Implementation of Forward/Backprop algorithm using numpy)

Fasttext subword embeddings that can backprop