In the last couple of years, machine learning has opened up new horizons in a wide range of industries, with advanced use cases emerging: Facebook’s facial recognition, Netflix’s recommended movies, PrismaAI’s image style transfer, Siri’s voice recognition, Google Allo’s natural language processing, and the list goes on.

Alongside these use cases are tons of fantastic open-source machine learning projects hosted on GitHub. Below are a list of the most popular projects. I hope you learn something new and always stay inspired.

TensorFlow is an open-source machine learning library for research and production. TensorFlow offers APIs for beginners and experts to develop for desktop, mobile, web, and cloud.

Originally developed by the Google Brain team for internal Google use, TensorFlow was released under the Apache 2.0 open source license on November 9, 2015.

The latest version of TensorFlow supports Keras, which is high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. There are also interfaces for Javascript and Swift.

Scikit-learn was originally written by David Cournapeau at a Summer Google of Code event. Its name more or less references a SciPy Toolkit. Scikit-learn has simple and efficient tools for data mining and data analysis, built on NumPy, SciPy, and Matplotlib. It’s a popular choice to use alongside TensorFlow because of its simplicity and handy functions.

Besides TensorFlow, Keras, and Scikit-learn, there is also the MXNet deep learning framework from Apache . It’s designed for both efficiency and flexibility—it allows you to mix symbolic and imperative programming to maximize efficiency and productivity. There is a model zoo you can visit for many models implemented in MXNet.

This list would be incomplete without the mention of PyTorch, another popular choice among machine learning enthusiasts. PyTorch is based on Torch and was distributed by Facebook as their machine learning framework. PyTorch is a Python package that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration and deep neural networks built on a tape-based autograd system.

Magenta is a research project exploring the role of machine learning in the process of creating art and music. Primarily this involves developing new deep learning and reinforcement learning algorithms for generating songs, images, drawings, and other materials. But it’s also an exploration in building smart tools and interfaces that allow artists and musicians to extend (not replace!) their processes using these models.

The main repo is for use with Python, but for the Javascript version there is also magenta.js. Magenta is a perfect project to showcase advanced machine learning uses most of us never think of. To be amazed by Magenta, check out some demos with sound and drawing generators.

This project is aimed to colorize line art. The AI can paint on a sketch according to a given color style, create its own color style to paint on a sketch, or transfer another illustration’s style.

There are some new features like image anchor and image transition, which are worth taking a look at.

This project has two components—CycleGAN and pix2pix—which contain PyTorch implementations for both unpaired and paired image-to-image translation. At first, it looks like another fairly ordinary style transfer solutions, but it can do some different things, like convert a horse to a zebra or from live photo to a Monet. And process is fast enough that it can be used on live video.