#machine learning

Companies are racing to find programmers capable of coding for ML and deep learning but, as Googlers Nikhil Thorat and Daniel Smilkov point out, “the development of ML systems is often restricted to those with computational resources and the technical expertise to work with commonly available ML libraries.” Enter deeplearn.js, an open source JavaScript library for machine learning.

Just off a stint at Baidu’s AI Group, Andrew Ng is here to bring artificial intelligence to the masses. Deeplearning.ai and Coursera are teaming up to bring a new sequence of online classes to help you learn to code AI.

Not content with beating humans at quiz shows, IBM is moving forward with its Watson Machine Learning service. Now generally available after a year’s worth of beta testing, WML promises to address the needs of both data scientists and devs.

These days, it seems that tech companies can’t hire their AI or ML specialists fast enough. So if you’re looking to upgrade your skillset or just fiddle around with a cool new tool, we’ve got you covered with our top 5 picks for the best open-source tools for machine learning.

Machine learning is the future. If, of course, we can teach computers to understand the signal from the noise. Neural networks are learning to do amazing things from recognizing images to driving and even coming up with recipes. But maybe we should hold off on hiring robots to replace our chefs.

Mesosphere DC/OS 1.9 adds three key new sets of features to simplify the process of building and deploying data-rich applications.

Companies are scrambling to find enough programmers capable of coding for ML and deep learning. Are you ready? Here are five of our top picks for machine learning libraries for Java.

It’s been one year since Yahoo open-sourced CaffeOnSpark so the tech giant has found a way to celebrate it — by open-sourcing TensorFlowOnSpark, its latest open source framework for distributed deep learning on big data clusters.

Machine learning experts are in high demand right now. As tech giants rely heavily on machine learning and AI these days, it comes as no surprise that their ML hiring spree has intensified. If you want to jump on the ML bandwagon, you’ll need the right tools.

Hillary Clinton’s 18-month campaign as the Democrat candidate for the presidency of the United States has been famously and resoundingly data-driven. A war room of senior analysts, mathematicians and researchers monitored, tracked and calculated every significant movement of her campaign, adjusting strategy to shift focus to different geo-political demographics. The polls gave affirmation that she was on track to win until the final hours of the campaign, but ultimately the pollsters failed to forecast the US election result correctly. A loss not just for Clinton and the Democrats, but for all those who are proponents of the value of data. Or so it is told.

Machine learning may sound futuristic, but it’s not. Speech recognition systems such as Cortana or Search in e-commerce systems have already shown us the benefits and challenges that go hand in hand with these systems. In our machine learning series we will introduce you to several tools that make all this possible. Next stop: MOA, an open source software specific for machine learning/data mining on data streams in real time.

Machine learning may sound futuristic, but its not. Speech recognition systems such as Cortana or Search in e-commerce systems have already showed us the benefits and challenges that go hand in hand with these systems. In our machine learning series we will introduce you to several tools that make all this possible. Next stop: Weka.

Machine learning may sound futuristic, but it is not. Speech recognition systems such as Cortana or Search in e-commerce systems have already showed us the benefits and challenges that go hand in hand with these systems. In our machine learning series we introduced you to several tools that make all this possible. Now it’s time to allow software developer Adam Geitgey to talk about the ABCs of machine learning and teach you how to make use of ML.

The amount of JavaScript being used on websites has tripled in the past three years. Machine learning is what web developers need to speed up their increasingly bulky applications, says product management VP Peter Blum.