21 Lessons, Get Started Building with Generative AI

Python package built to ease deep learning on graph, on top of existing DL frameworks.

VIP cheatsheets for Stanford's CS 229 Machine Learning

A Stable Diffusion Demo with React and FastAPI

Classical equations and diagrams in machine learning

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

👋 Xplique is a Neural Networks Explainability Toolbox

Papers and code of Explainable AI esp. w.r.t. Image classificiation

List of Computer Science courses with video lectures.

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

Implementation code for the paper "Graph Neural Network-Based Anomaly Detection in Multivariate Time Series" (AAAI 2021)

PyTorch Dual-Attention LSTM-Autoencoder For Multivariate Time Series

Latex code for making neural networks diagrams

Pytorch implementaion of UNet, Deep ResUnet and ONet models for the brain tumor segmentation task

The Tensorflow, Keras implementation of U-net, V-net, U-net++, UNET 3+, Attention U-net, R2U-net, ResUnet-a, U^2-Net, TransUNET, and Swin-UNET with optional ImageNet-trained backbones.

PyTorch implementation of MTAD-GAT (Multivariate Time-Series Anomaly Detection via Graph Attention Networks) by Zhao et. al (2020, https://arxiv.org/abs/2009.02040).

RNN based Time-series Anomaly detector model implemented in Pytorch.

Code for Estimating Multi-cause Treatment Effects via Single-cause Perturbation (NeurIPS 2021)

Simple PyTorch implementations of U-Net/FullyConvNet (FCN) for image segmentation

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

A Collection of Variational Autoencoders (VAE) in PyTorch.

Here I gather promising research directions to make DNNs interpretable

KDD 2019: Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network

A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.

Unsupervised time series anomaly detection library

pytorch tutorial for beginners

Model interpretability and understanding for PyTorch

This repository contains the implementation of Dynamask, a method to identify the features that are salient for a model to issue its prediction when the data is represented in terms of time series.…

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @…

Interesting resources related to XAI (Explainable Artificial Intelligence)