awesome-decision-tree-papers

A collection of research papers on decision, classification and regression trees with implementations.

awesome-deepbio

A curated list of awesome deep learning applications in the field of computational biology

bayerclaw

BayerCLAW workflow orchestration system for AWS

brianeads

Config files for my GitHub profile.

chatgpt-data-science-prompts

A repository of 60 useful data science prompts for ChatGPT

compositperterbanalysis

The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.

deep-learning-drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

deep-residual-learning-for-image-recognition

Implementation of https://arxiv.org/pdf/1512.03385.pdf

deep_learning_examples

Examples of using deep learning in Bioinformatics

deeplearning-in-bioinformatics

For anyone who are eager to applying deep learning in bioinformatics!

gt4sd-core

GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process.

hnmt

Helsinki Neural Machine Translation system

interpret

Fit interpretable models. Explain blackbox machine learning.

isoreader

Read IRMS (Isotope Ratio Mass Spectrometry) data files into R

machine-learning-collection

:closed_book:machine learning tech collections at Microsoft and subsidiaries.

mathart

Create mathematical art with R

meshcnn

Convolutional Neural Network for 3D meshes in PyTorch

moby

Moby Project - a collaborative project for the container ecosystem to assemble container-based systems

sagemaker-studio-auto-shutdown-extension

tensor2robot

Distributed machine learning infrastructure for large-scale robotics research

tensorflow2-generative-models

Implementations of a number of generative models in Tensorflow 2. GAN, VAE, Seq2Seq, VAEGAN, GAIA, Spectrogram Inversion. Everything is self contained in a jupyter notebook for easy export to colab.

tensorwatch

Debugging, monitoring and visualization for Python Machine Learning and Data Science

tybalt

Training and evaluating a variational autoencoder for pan-cancer gene expression data

vdcnn

Implementation of Very Deep Convolutional Neural Network for Text Classification