Weekly Machine Learning Opensource Roundup – July 26, 2018


SoC Star Modelling
A project to make a model which given an image of the night sky, identifies the constellations present in it using a CNN-classifier.

A series of experiments on using Compositional Pattern Producing Networks and GANs. See the two part blog posts here and here.


Scalable, cross-platform ion-electron app that allows the user to annotate an image dataset.

Use SQL on various data sources


A PyTorch implementation of the architecture of Mask RCNN, serves as an introduction to working with PyTorch

Tensorflow implementation of a Neural Turing Machine

Implementation of “StyleBank An Explicit Representation for Neural Image Style Transfer”

An implementation of super-resolution based on dual-path networks. It can be used directly to reconstruct your low-resolution image to high-resolution.

GluonNLP provides implementations of deep learning models in NLP, and build blocks for text data pipelines and models.


Scikit-learn style model finetuning for NLP. Finetune ships with a pre-trained language model from Improving Language Understanding by Generative Pre-Training” and builds off the OpenAI/finetune-language-model repository.

Parallelizing AdaBoost on Multi Core Machines using open MP in C++
AdaBoost, short for Adaptive Boosting, is a type of boosting algorithm which combines several weak classifiers to create one strong classifier. This project contains a multi-core parallel implementation of adaboost algo in C++.

Torchbearer is a PyTorch model training library designed by researchers, for researchers. Specifically, if you occasionally want to perform advanced custom operations but generally don’t want to write hundreds of lines of untested code then this is the library for you.

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