ML.NET, Microsoft’s open source machine learning framework, has been updated to version 1.2. Here’s a quick rundown of the updates. Read the article on Visual Studio Magazine to find out more.

  • General availability of TimeSeries support for forecasting and anomaly detection:
  • General availability of ML.NET packages to use TensorFlow and ONNX models:
  • Easily integrate ML.NET models in web or serverless apps with Microsoft.Extensions.ML integration package (preview):
  • ML.NET CLI updated to 0.14 (preview):
  • Model Builder updates:
    • Expanding support to .txt files and more delimiters for values

    • No limits on training data size

    • Smart defaults for training time for large datasets

    • Improved model consumption experience

The Data Science & AI community has truly embraced open source. In fact, there are so many libraries and tools out there, that it can be challenging to keep up. Fortunately, here’s a great round up of 21 open source tools for Machine Learning. Some of them you may have heard of, but I guarantee there are a few surprises in this list, even for the seasoned expert.

  • Presenting 21 open source tools for Machine Learning you might not have come across
  • Each open-source tool here adds a different aspect to a data scientist’s repertoire
  • Our focus is primarily on tools for five machine learning aspects – for non-programmers(Ludwig, Orange, KNIME), model deployment(CoreML, Tensorflow.js), Big Data(Hadoop, Spark), Computer Vision(SimpleCV), NLP(StanfordNLP), Audio, and Reinforcement Learning(OpenAI Gym)

Machine learning is capable of doing some amazing things. However, the state of the art tends to be limited to academic and large corporate institutions. What would happen if artists, filmmakers, and the creative community had access to cutting edge technology without the heavy investment in research and development.

The Verge looks into just that.

Say you’re an animator on a budget who wants to turn a video of a human actor into a 3D model. Instead of hiring expensive motion capture equipment, you could use Runway to apply a neural network called “PosetNet” to your footage, creating wireframe models of your actor that can then be exported for animation.

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.

Federated Learning (FL) is a distributed approach to machine learning that enables training on a large corpus of decentralized data residing on devices like mobile phones.  FL employs the approach of “bringing the code to the data, instead of the data to the code.” Additionally, it addresses the fundamental problems of privacy, ownership, and locality of data.

Here’s a more in depth look at the approach.

There’s a good high-level overview of federated learning on Google’s AI blog. Devices download the current model, improve it by learning using data local to the phone, and then send a small focused model update back to the cloud, where it is averaged with other user updates to improve the shared model. No individual updates are stored in the cloud, and no training data leaves the device.

Machine learning is no longer just for data science whiz kids. Now, front end developers need to have a basic handle on this technology. Here’s a great talk by Charlie Gerard on “Practical Machine Learning for Front End Developers.”

From the abstract:

Machine learning can have some pretty complicated concepts to grasp if you’re not a data scientist. However, recent developments in tooling make it more and more accessible for developers and people with little or no experience. One of these advancements is the ability to now train and run machine learning algorithms and models in the browser, opening this world to front-end developers to learn and experiment. In this presentation, we will talk about the different applications, possibilities, tools and resources, as well as show a few examples and demos, so you can get started building your own experiments using machine learning in JavaScript.

Here’s a great explanation of Reinforcement Learning, AlphaGo Zero, and how it compares to other forms of machine learning.

For example, AlphaGo, in order to learn to play (the action) the game of Go (the environment), first learned to mimic human Go players from a large data set of historical games (apprentice learning). It then improved its play through trial and error (reinforcement learning), by playing large numbers of Go games against independent instances of itself.