Finding the pitfalls of deep learning predictors of interacting residues in antibodies 🦠

MagicSoup is a game that simulates cell metabolic and transduction pathway evolution. Define a 2D world with certain molecules and reactions. Add a few cells and create evolutionary pressure by sel…

Panel: The powerful data exploration & web app framework for Python

Illustrations of antibodies created using excalidraw

a versatile video renderer

TypeDB Loader - Data Migration Tool for TypeDB

PAthological QUpath Obsession - QuPath and Python conversations

Code for training and inference for multitask graph convolutional networks

A carefully crafted Org exporter back-end for Hugo

Org mode parser with html & pretty printed org rendering. also shitty static site generator.

Repository to host the Docker images files used in conda-forge

Miscellaneous "recipes" for the Conda packaging system.

dplyr-style piping operations for pandas dataframes

Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award).

Microsoft Distributed Machine Learning Toolkit

Interactive data exploration with Altair

Automatic extraction of relevant features from time series:

Code and models accompanying "Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning"

Python wheels that work on any linux (almost)

Open source time series library for Python

SFrame: Scalable tabular and graph data-structures built for out-of-core data analysis and machine learning.

DeepVis Toolbox

Code & Models for Temporal Segment Networks (TSN) in ECCV 2016

This is the implementation of paper Model Free Episodic Control

reproducible executable environments

Source code for paper: Learning to Track at 100 FPS with Deep Regression Networks, Held, et al. ECCV 2016

Bringing up some extra Cosmo to Keras.

Attempt at implementing system described in "Neural Turing Machines." by Graves, Alex, Greg Wayne, and Ivo Danihelka. (http://arxiv.org/abs/1410.5401)

Code for reproducing results of NIPS 2014 paper "Semi-Supervised Learning with Deep Generative Models"