Weekly Machine Learning Opensource Roundup – Jan. 18, 2018


Migrating to Python 3 with pleasure
A short guide on features of Python 3 for data scientists

Production Data Science
Workflow for collaborative data science aimed at production

Computer Vision Basics with Python Keras and OpenCV
Full tutorial of computer vision and machine learning basics with OpenCV and Keras in Python.

Pirate AI
PirateAI trains autonomous agents in a simulated environment on Unity, Hyperopt, and Keras

CapsNet Tensorflow
Testing Capsule Network on various datasets

TVM Mali
Optimizing Mobile Deep Learning on ARM GPU with TVM


Spinzero – Jupyter Notebook Theme
A minimal Jupyter Notebook theme

Label Maker
Data Preparation for Satellite Machine Learning

A platform to visualize the deep learning process.

Apache Sentry
A granular, role-based authorization module for Hadoop. Sentry provides the ability to control and enforce precise levels of privileges on data for authenticated users and applications on a Hadoop cluster


TensorFlow implementation of CipherGAN

Machine Learning-Based Predictive Modelling of CRISPR/Cas9 guide efficiency. Microsoft Research presents a state-of-the art predictive approach to modeling which RNA guides will effectively perform a gene knockout by way of the CRISPR/Cas9 system.

Gradient Checkpointing
Training very deep neural networks requires a lot of memory. Using the tools in this package, developed jointly by Tim Salimans and Yaroslav Bulatov, you can trade off some of this memory usage with computation to make your model fit into memory more easily.


.NET bindings for native numerical computing

Simple Node.js API for robust face detection and face recognition.

A GPU-backed numpy-like infrastructure for scientific computing in JavaScript. Propel runs both in the browser and natively from Node.

A light-weight column-oriented data processing engine based on a new data model and is an alternative to conventional SQL-like languages, map-reduce and other set-oriented approaches

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