This is a curated list of research papers, resources and tools related to using Graph Neural Networks (GNNs) for drug discovery.

A reading list for deep graph learning acceleration.

Code for building and running containers available on AWS marketplace

Machine Learning Engineering Open Book

T2I-Adapter

Neuroscope:An Explainable AI Toolbox for Semantic Segmentation and Image Classification of Convolutional Neural Nets

Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities

A probabilistic approach from an Improbabilistic company

Getting started with diffusion

High-Resolution Image Synthesis with Latent Diffusion Models

IPyPlot is a small python package offering fast and efficient plotting of images inside Python Notebooks. It's using IPython with HTML for faster, richer and more interactive way of displaying big …

Training models for basecalling Oxford Nanopore reads

Benchmarks for popular CNN models

Models and examples built with TensorFlow

Open source platform for the machine learning lifecycle

TensorFlow - A curated list of dedicated resources http://tensorflow.org

Deep Replay - Generate visualizations as in my "Hyper-parameters in Action!" series!

Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization

Another Wes Anderson palette implementation

Start Tensorboard in Jupyter Notebook

A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.

Interactive tools and developer experiences for Big Data on Google Cloud Platform.

An R package for causal inference in time series

Statsmodels: statistical modeling and econometrics in Python

Burrow-Wheeler Aligner for short-read alignment (see minimap2 for long-read alignment)

The Leek group guide to data sharing

Various Dockerfiles I use on the desktop and on servers.

A Wes Anderson color palette for R

Computer Algebra System written in JavaScript.