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awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

bayesgrad

BayesGrad: Explaining Predictions of Graph Convolutional Networks

biocompoundml

BioCompoundML is a software tool for rapidly screening chemicals by chemical properties, using machine learning.

biofuels

boruta_py

Python implementations of the Boruta all-relevant feature selection method.

cfd_unet

Unet application to CFD data

chemical_vae

Code for 10.1021/acscentsci.7b00572, now running on Keras 2.0 and Tensorflow

chorus

A simple chemical structure graph modeling, drawing and analysis toolkit

cs231n.github.io

Public facing notes page

data-science-ipython-notebooks

Continually updated data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines. https://bit.ly/data-notes

deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

evolutionary-computation-course

Jupyter/IPython notebooks about evolutionary computation.

ffnn

Feedforward Neural Network in Python

follow-the-ridge

Minimax Optimization, Stackelberg Games, Generative Adversarial Networks

galaxytools

:microscope::books: Galaxy Tool wrappers

gan-optimization-landscape

code to reproduce the empirical results in the research paper

gans

GAN_exploration

geneticalgorithmfeatureselection

A simple example of how a genetic algorithm can be used to select the optimal subset of features to use for machine learning problems.

geneticalgorithmswithpython

source code from the book Genetic Algorithms with Python by Clinton Sheppard

graph_nn

GNN collection

guacamol

Benchmarks for generative chemistry

handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

ibenchmark

Benchmark interpretation of QSAR models

interpret

Fit interpretable models. Explain blackbox machine learning.

keras-neural-graph-fingerprint

Keras implementation of Neural Graph Fingerprints as proposed by Duvenaud et al., 2015

ktp_test

computational chemistry job test

lime

Lime: Explaining the predictions of any machine learning classifier

lstm

mkdocs-rc-docs

Experimental Research Computing documentation generation using mkdocs

mlcourse_open

OpenDataScience Machine Learning course. Both in English and Russian