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Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
Algorithms for explaining machine learning models
This will show how to make autoencoders using pytorch neural networks
A curated list of awesome alfred workflows
A curated list of awesome architecture search resources
List of resources for bayesian inference
A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.
A collection of resources and papers on Diffusion Models and Score-based Models, a darkhorse in the field of Generative Models
A collection of research papers and software related to explainability in graph machine learning.
Papers about explainability of GNNs
Awesome Graph Self-Supervised Learning
Awesome Incremental Learning
A curated list of awesome Machine Learning frameworks, libraries and software.
Curated list of known efforts in materials informatics
Awesome papers related to generative molecular modeling and design.
A curated list of Monte Carlo tree search papers with implementations.
A list of awesome resources on normalizing flows.
Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html
Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).
A curated list for awesome self-supervised learning for graphs.
This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.
A curated list of programmatic weak supervision papers and resources
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch
A framework for Bayesian model selection (BMS) and Bayesian model Averaging (BMA).
Experiments in Bayesian Machine Learning
A curated list of the latest breakthroughs in AI (in 2021) by release date with a clear video explanation, link to a more in-depth article, and code.
A simple and extensible library to create Bayesian Neural Network layers on PyTorch.
Python implementations of the Boruta all-relevant feature selection method.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.