Stars
Minimal and clean examples of machine learning algorithms implementations
It is said that, Ilya Sutskever gave John Carmack this reading list of ~ 30 research papers on deep learning.
Basic .NET MAUI application demonstrating the use of an onnx model as part of a cross-platform mobile application.
Graph Neural Network Library for PyTorch
A curated list of the latest breakthroughs in AI (in 2022) by release date with a clear video explanation, link to a more in-depth article, and code.
Must-read papers on graph neural networks (GNN)
Romanian benchmark leaderboard
PyTorch implementation of FIM and empirical FIM
Sequence modeling benchmarks and temporal convolutional networks
Model summary in PyTorch similar to `model.summary()` in Keras
This repository includes the code utilized for real-time diagnostics of plasma devices based on machine learning methods.
A topic-centric list of HQ open datasets.
MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
An Open Source Machine Learning Framework for Everyone
Machine learning, research, reading group, Transilvania University of Brasov
Parallel Bayesian ArtMap Classifier Python Code
General code to convert a trained keras model into an inference tensorflow model
PySpark + Scikit-learn = Sparkit-learn
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
Weka package for parameter optimization, similar to GridSearch, but with arbitrary number of parameters.
Python library for multilinear algebra and tensor factorizations
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)