2018 was a banner year for machine learning on GitHub. Projects like TensorFlow and PyTorch ranked among some of the most popular on the site, while Python carried on its dominance as a top programming language. It looks like the Octoverse is all about ML and we are 100% here for it.
Need a good math tutor? Julia’s the name and differential equations is the new game. Julia’s latest library combines machine learning with solving differential equations. This collaborative effort shows off the power that Julialang has as a platform for machine learning.
Given the acceleration of change and increasing complexity of machine learning today, we can see many cases of high-profile samples of ML models not working as intended. In this article, SpringPeople Software shares some suggestions on how to make ML a better place.
Machine learning is complicated, but it’s becoming easier and easier to grasp with new tools and platforms. In preparation for AWS re:Invent 2018, AWS updated SageMaker. What are the newest features?
New ways of handling large amounts of data by building more layers of artificial intelligence into computer systems have been allowing developers and businesses to create computer systems that work for them. In this article, Paul Bates explains why the future of consumerism and business optimization relies on machine learning and what role developers play in all this.
Last week, we covered artificial intelligence in our tech history course. This week, we dive deep into the hottest trend in this field: machine learning. ML’s near-human performance in tasks like image recognition masks some really strange issues, because ML logic is not like our Earth logic.
We have some more machine learning news for you. Meet Kalimdor, a Typescript based machine learning library that promises to solve your ML problems as well as teach you how ML algorithms work.
Many of the most exciting high-tech projects nowadays include bringing together knowledge from two or more well-established and fast-growing fields. One of the prominent examples is applying machine learning in order to filter and analyze the huge amount of data we obtain from the Internet of Things (IoT). But first, let’s see why IoT needs help from artificial intelligence in order to reach its full potential.
The open source machine learning frameworks just keep on coming. Oryx 2 is focused on real-time, large scale machine learning and uses the power of 3 tiers. Grab it by the horns and create custom applications.
Machine learning’s growth continues as it permeates into unrelated industries. Travel booking might not seem like a good fit at first, but Wilco van Duinkerken of trivago explains how ML is innovating the way you find and book your next holiday.
Oracle has just released a new open source tool: GraphPipe is designed to simplify and standardize the deployment of machine learning models. We talked to Vish Abrams, Architect, Cloud Development at Oracle about the new tool, its benefits, challenges and more.
Did you know you can count bees with AI? We take a look at some real world use cases for machine learning that you might have missed.
Are you ready for machine learning? Do you have a plan? In this article, Atakan Cetinsoy from BigML goes over six things that every organization needs to be aware of when they’re devising their own machine learning strategy.
How can developers learn to utilize machine learning in their DevOps practice? In this article, Prasanthi Korada goes over some basic approaches that can help developers apply cutting edge tech like machine learning to their everyday work.