Apache MXNet 1.1.0 has been released. Apache MXNet is a deep learning framework for training and prediction and can be used with APIs for multiple programming languages, such as Python, R, Scala, and C++. For example, the Python or R API can be used to train neural network models and then MXNet can be used to integrate those models into an application.

The latest release helps developers build vocabulary and pre-trained word embeddings. It also adds ‘sparse.dot’ operator for sparse tensor support and improves user experience.

Google offers machine learning crash course to the public
Google is continuing its effort to democratize artificial intelligence with its Machine Learning Crash Course (MLCC), which is now available to the public. The course was previously taken by more than 18,000 Google employees and it was one of the company’s most popular courses. The course will cover machine learning fundamentals such as loss and gradient descent, classification models, and neural nets.

In addition, the course will include Python programming exercises, but non-technical users will be able to skip this section.

Algorithmia designs the DanKu protocol to evaluate machine learning models
In order to provide a way to evaluate and purchase machine learning models on public blockchains, Algorithmia has introduced the DanKu protocol. DanKu is designed to provide access high quality machine learning models. It uses blockchain technology with smart contracts, allowing anyone to post a data set, evaluate function, and provide a monetary reward for the one who provides the best trained model for that data.

The DanKu protocol allows for the creation of a decentralized marketplace for machine learning models, providing data scientists with an opportunity to monetize their skills. “Since the DanKu protocol does not require trust, it removes the need for a middleman for exchanging ML models. This game changing feature will further democratize access to Machine Learning for the masses. Hopefully this will create an uptick in open-source ML models available for everyone,” the company wrote in a post.

PhantomJS is being abandoned
The team behind PhantomJS has announced the project is coming to an end and will be archived. The current plans for PhantomJS 2.5 and 2.1.x, and its source and binary packages will be abandoned immediately. The master branch will remain, allowing developers to continue improving PhantomJS on their own. According to the team, if they decided to pick up development on the project again, it will be unarchived.