Stars
A collection of awesome bio-foundation models, including protein, RNA, DNA, gene, single-cell, and so on.
Integrating histology and spatial transcriptomics - NeurIPS 2024
Prov-GigaPath: A whole-slide foundation model for digital pathology from real-world data
Command-line tool to inspect the difference between (the text in) two PDF files
Implementation of 'DIVA: Domain Invariant Variational Autoencoders'
disentanglement_lib is an open-source library for research on learning disentangled representations.
A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023
Companion webpage for the book "Bayesian Optimization" by Roman Garnett
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
Google's Engineering Practices documentation
A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
A single-cell ATAC-seq simulation framework (R package)
Formalizing and benchmarking open problems in single-cell genomics
A package to help convert different single-cell data formats to each other
A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.
This repository is meant to serve as an opinionated, pedagogical guide on software engineering best practices for those of us in machine learning. Follow along with the guide here: https://johngior…
scRNAseq analysis notes from Ming Tang
Generates a simple or multiple linear model and outputs associated values and statistics, i.e SSReg, RSS, SST, MSReg, etc...
Easily install and load packages from the tidyverse
An Open Source Machine Learning Framework for Everyone
Google Chrome, Firefox, and Thunderbird extension that lets you write email in Markdown and render it before sending.