What’s cooler than machine learning? Machine learning that’s made by machines. In Tile, a new machine learning language from Vertex.AI, crucial support structures are automatically generated to save time and effort.
It is always fascinating to see the versatile ways in which machine learning can be used. At Outfittery, algorithms help the experts select the most suitable outfits for customers — quite literally. In an interview at W-JAX 2017 in Munich, Jesper Richter-Reichhelm, CTO at Outfittery GmbH, explains how the company uses machine learning and which frameworks they use. He also tells us who makes better suggestions — human beings or machines.
TensorFlow 1.4 is here! The latest update to one of the most popular open source machine learning projects boasts big changes, new features, and even a couple of bug fixes.
There’s a new, free, and open-source Python library for automatic differentiation! Tangent —an experimental release— offers extra autodiff features other Python machine learning libraries don’t have. Plus, it’s compatible with TensorFlow and NumPy.
Looking to improve your ML skills? Why not take a look at some of the most popular open source machine learning projects on GitHub? We’re taking a closer look at the top five projects to the state of open source machine learning.
Our first ML Conference will debut in December in Berlin. Until then, we’d like to give you a taste of what’s to come. We talked with, Dr. Katleen Gabriels, Assistant Professor at Eindhoven University of Technology about how algorithms influence our daily lives and why ethics are essential to the development of machine learning.
RebelLabs’ Developer Productivity Report 2017 showed that Kotlin is the most beloved programming language and they’re not wrong — as it turns out, Stack Overflow measured programming languages’ popularity and reached the same conclusion. Coincidence? Perhaps not.
The Apache Software Foundation is opening up the field of machine learning with its new open source project, PredictionIO. But how are they making it easier for newcomers to learn this devilishly complicated bit of coding? The clever use of templates, of course.
Our first ML Conference will debut in December in Berlin. Until then, we’d like to give you a taste of what’s to come. We talked with, Markus Ehrenmüller-Jensen, Business Intelligence Architect at Runtastic about how the company involves machine learning into their daily business, the benefits, the battle scars and everything in between. Also, you’ll get a sneak peek at his talk.
The CERT Division of Carnegie Mellon University’s Software Engineering Institute has published an updated list of technologies that might give us headaches in the security department. Both machine learning and blockchain have made the cut but that’s actually not a comforting thought, especially since the former is one of the three domains that must be considered high priority for outreach and analysis in 2017.
Machine learning is the hottest tech trend these days. Now, the barriers to entry are lower than ever with Google’s Teachable Machine from the A.I. Experiment, which promises to help users learn about machine learning… without using a single line of code.
It’s safe to say that Python is everywhere we turn, from DevOps to machine learning and data science. Stack Overflow seems to agree — according to their calculations, Python is the fastest growing programming language right now. We talked with David Robinson, Data Scientist at Stack Overflow about Python’s growth and the possible reasons behind it.
Machine Learning is the next big thing. Are you ready? The ML Conference 2017 program is now live! Here’s a sneak peek of some of the awesome sessions, workshops, and keynotes that are already scheduled. Book your tickets now and save up to €180!
Eclipse Picasso is a free open-source DNN visualization tool that gives you partial occlusion and saliency maps with minimal fuss. In this article, Ryan Henderson explains some of the problems that arise with machine learning and how you can avoid them with Eclipse Picasso.