TerraMind is the first any-to-any generative foundation model for Earth Observation, built by IBM and ESA.

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

A framework where a deep Q-Learning Reinforcement Learning agent tries to choose the correct traffic light phase at an intersection to maximize traffic efficiency.

Explanation to key concepts in ML

DiffLinker: Equivariant 3D-Conditional Diffusion Model for Molecular Linker Design

course.fast.ai 2022 part 2

Question generation using state-of-the-art Natural Language Processing algorithms

A complete speech segmentation system using Kaldi and x-vectors for voice activity detection (VAD) and speaker diarisation.

PyG re-implementation of Neural Bellman-Ford Networks (NeurIPS 2021)

Papers about explainability of GNNs

Official implementation of Neural Bellman-Ford Networks (NeurIPS 2021)

The property-based testing library for Python

A library for graph deep learning research

Prettify Python exception output to make it legible.

A powerful and flexible machine learning platform for drug discovery

Graphormer is a general-purpose deep learning backbone for molecular modeling.

Code for the paper "How Attentive are Graph Attention Networks?" (ICLR'2022)

Large scale graph learning on a single machine.

OpenBioLink is a resource and evaluation framework for evaluating link prediction models on heterogeneous biomedical graph data.

Generate embeddings from large-scale graph-structured data.

An Open-Source Package for Knowledge Embedding (KE)

🤖 A Python library for learning and evaluating knowledge graph embeddings

Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org

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

A collection of research papers and software related to explainability in graph machine learning.

This repository implements and evaluates convolutional networks on the Möbius strip as toy model instantiations of Coordinate Independent Convolutional Networks.

Kubeflow’s superfood for Data Scientists

All Algorithms implemented in Python

🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support