It can become overwhelming as a data scientist to simply keep track of all that’s happening in machine learning. This series of blog posts takes that pain away by highlighting the top ML GitHub repos each month. Check out the top 6 machine learning GitHub repositories created in June.

There’s a heavy focus on NLP again, with XLNet outperforming Google’s BERT on several state-of-the-art benchmarks. All machine learning GitHub repositories are open source; download the code and start experimenting! Introduction Do you sometimes feel that […]

MIT has unveiled an artificial intelligence system that it said could make an array of AI techniques more accessible to programmers, while also offering adding value to experts.

Researchers said the system, called Gen, is similar to TensorFlow, a set of tools developed by Google for automating AI tasks, principally those involved with deep learning and neural networks.

InfoWorld write a glowing review of TensorFlow 2.

Of all the excellent machine learning and deep learning frameworks available, TensorFlow is the most mature, has the most citations in research papers (even excluding citations from Google employees), and has the best story about use in production. It may not be the easiest framework to learn, but it’s much less intimidating than it was in 2016. TensorFlow underlies many Google services.

As use of Machine Learning (ML) in medicine becomes more common, it has the power to transform healthcare. One particularly disruptive example is Cancer Detection and Analysis. Here’s a closer look at how ML helps in this area.

by examining biological data such as DNA methylation and RNA sequencing can then be possible to infer which genes can cause cancer and which genes can instead be able to suppress its expression.

We’re right in the thick of baseball season here in the US, which makes this video on ML particularly timely. Baseball is known for it’s sometimes ridiculous hand signals to relay messages to players in the field. Can AI decode these signals? Spoiler alert: yes.

You have to wonder how long it will be before this technology will be deployed at the major league level, if it hasn’t been already.