jmrinaldi

sagemaker-distributed-training-workshop

Hands-on workshop for distributed training and hosting on SageMaker

graphein

Protein Graph Library

alphafold

Open source code for AlphaFold.

pyprobml

Python code for "Probabilistic Machine learning" book by Kevin Murphy

dannce

google-research

Google Research

deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

symbolic_rxn

Integrating Deep Neural Networks and Symbolic Inference for Organic Reactivity Prediction

kaggle-rcic-1st

1st Place Solution for Kaggle Recursion Cellular Image Classification Challenge -- https://www.kaggle.com/c/recursion-cellular-image-classification/

BatchEffectRemoval2018

DeepExplain

A unified framework of perturbation and gradient-based attribution methods for Deep Neural Networks interpretability

genomelake

Simple and efficient access to genomic data for deep learning models.

convex_adversarial

A method for training neural networks that are provably robust to adversarial attacks.

TCN

Sequence modeling benchmarks and temporal convolutional networks

dragonn

A toolkit to learn how to model and interpret regulatory sequence data using deep learning.

draft-class-defenders-ml

Using machine learning to predict the best defenders in the 2018 draft class

finetune-transformer-lm

Code and model for the paper "Improving Language Understanding by Generative Pre-Training"

sentiment-discovery

Unsupervised Language Modeling at scale for robust sentiment classification

fastdeepnets

tybalt

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

UNIT

Unsupervised Image-to-Image Translation

deep-retina

deep-retina is a project to build a convolutional neural network that can predict retinal ganglion cell responses to natural stimuli with high accuracy.

nucleus

Python and C++ code for reading and writing genomics data.

chess-alpha-zero

Chess reinforcement learning by AlphaGo Zero methods.

Keras-GAN-1

Keras implementations of Generative Adversarial Networks.

ML-epitopes-prediction

This repository describe the usage of neural networks to predict the affinity of peptides to MHC type I for humans. Neural networks was written in tensorflow and keras

keras-molecules

Autoencoder network for learning a continuous representation of molecular structures.

gans

Generative Adversarial Networks implemented in PyTorch and Tensorflow

pytudes

Python programs to practice or demonstrate skills.

tf-vqvae

Tensorflow Implementation of the paper [Neural Discrete Representation Learning](https://arxiv.org/abs/1711.00937) (VQ-VAE).