Stars
Machine Learning Engineering Open Book
A library to inspect and extract intermediate layers of PyTorch models.
The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."
Tensors and Dynamic neural networks in Python with strong GPU acceleration
A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)
Graph Neural Network Library for PyTorch
Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.
FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs o…
Evolutionary Scale Modeling (esm): Pretrained language models for proteins
Generate embeddings from large-scale graph-structured data.
Trinity RNA-Seq de novo transcriptome assembly
An open library for the analysis of molecular dynamics trajectories
Sequential regulatory activity predictions with deep convolutional neural networks.
Tool for the Quality Control of Long-Read Defined Transcriptomes
Robust representation of semantically constrained graphs, in particular for molecules in chemistry
Graph Neural Networks with Keras and Tensorflow 2.
Python package for graph neural networks in chemistry and biology
A modular framework for neural networks with Euclidean symmetry
Therapeutics Commons (TDC): Multimodal Foundation for Therapeutic Science
FAIR Chemistry's library of machine learning methods for chemistry
fastrl is a reinforcement learning library that extends Fastai. This project is not affiliated with fastai or Jeremy Howard.
📺 Discover the latest machine learning / AI courses on YouTube.
An elegant PyTorch deep reinforcement learning library.
A scanpy extension to analyse single-cell TCR and BCR data.
Workflow for creating and analyzing the Open Catalyst Dataset
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming (ICML'21)