Deep learning doesn’t have to stay science-fiction. When it comes to learning the latest ML, Keras is a great library for getting started and it can create some impressive feats. Where should you get started and why should you add it to your list of resolutions?
December is almost upon us so this is a good time to take a step back and look at some of the most trending technologies we saw in 2018, talk a bit about their status and what questions are raised about their prospects. Voting ends on Thursday!
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?
If you cannot or do not want to build an AI project from scratch, you have countless choices of ready-made services. But what can you do if the finished services do not fit the project? Customizable AI and ML models in the cloud, which you can train with your own data, provide a remedy.
Facebook is on a roll! The company recently announced that they would soon release some internal tools and they did not disappoint. The latest tool to be open sourced is Getafix, which learns from engineers’ past code fixes to recommend bug fixes. Getafix aims to let computers take care of the routine work under the watchful eye of a human. Let’s take a closer look.
Machine learning may have all sorts of use cases, but forecasting? In honor of the upcoming ML Conference, we talked to Philipp Beer about how data scientists can utilize ML in statistical forecasting. We talk about the advantages and disadvantages of modern vs. classical methods, how can one decide between the two, and where should they turn when they need good predictions for their business KPIs.
Companies are looking for more ML talent. Prove you have the machine learning knowledge to get a data science job in one of the best fields in the US. In this article, Yana Yelina explores four of the most common methods for ML algorithms.
It’s been three months since Kubeflow 0.2 was released so now it’s time for 0.3 to shine. This release provides easier deployment and customization of components and better multi-framework support. In this article, we’ll have a look at some of the highlights.
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.
Have you moved on to reinforcement learning for ML? This new approach to machine learning is now supported by Horizon, an end-to-end platform that has just been open-sourced by Facebook. Let’s broaden our horizons and see what Horizon has to offer!
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.
Deep Learning is all the hype these days, beating another record most every week but writing code for deep learning is not just coding – it really helps if you have a basic understanding of what’s going on beneath. In this session from last year’s ML Conference, Sigrid Keydana offers a quick lesson on deep learning, as well as some tips and tricks for developers who’d like to dip their toes into this topic.
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.