A playbook for systematically maximizing the performance of deep learning models.

Deciphering cell-cell communication from spatially resolved transcriptomic data at single-cell resolution with subgraph-based attentional graph neural network

Simple Python version management

CZ CELLxGENE Discover Census

Papers about explainability of GNNs

Benchmark datasets, data loaders, and evaluators for graph machine learning

AlphaFold 3 inference pipeline.

This repository contains all materials created to teach the Deep Learning Bootcamp 2024 at NTU

Dataframes powered by a multithreaded, vectorized query engine, written in Rust

The Rust Programming Language: Experimental Edition

Free, simple, and intuitive online database diagram editor and SQL generator.

Form builder for the Singapore Government

Model interpretability and understanding for PyTorch

Open source implementation of OPC UA (OPC Unified Architecture) aka IEC 62541 licensed under Mozilla Public License v2.0

Code at the speed of thought – Zed is a high-performance, multiplayer code editor from the creators of Atom and Tree-sitter.

Machine Learning Engineering Open Book

A React component for playing a variety of URLs, including file paths, YouTube, Facebook, Twitch, SoundCloud, Streamable, Vimeo, Wistia and DailyMotion

A new markup-based typesetting system that is powerful and easy to learn.

Mockoon is the easiest and quickest way to run mock APIs locally. No remote deployment, no account required, open source.

Authentication for the Web.

A dedicated scratchpad for developers

Sub-cortical brain tissue segmentation using CNN

Python wrapper to Philipp Krähenbühl's dense (fully connected) CRFs with gaussian edge potentials.

MetalSite: alignment-free metal ion-binding site prediction from protein sequence through pretrained language model and multi-task learning

The Fast Cross-Platform Package Manager

Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125

Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.

GPU environment and cluster management with LLM support

The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.

Reads key-value pairs from a .env file and can set them as environment variables. It helps in developing applications following the 12-factor principles.