Just in time for Halloween, researchers at Data61 and MIT Media lab have created a deep learning algorithm to generate disturbing imagery. There are two parts to the Nightmare Machine project – Haunted Places and Haunted Faces – which are each terrifying and impressive in equal measure. For Haunted Places the team used algorithms to learn what it called a'nightmarifying' process, learning a variety of spooky artistic styles that can then be applied to idyllic imagery. "We use deep learning algorithms to learn first how haunted houses, then ghost towns, and more recently toxic cities look," explains principal research scientist at Data61, Manuel Cebrian. "Then, we apply the learned style to famous landmarks. It's surprising how well the algorithm can extract the element from the "scary" templates and plant it into the landmarks."

Machine Learning is making its presence felt on the worldwide stage as a major driver of digital business success. A good proof of that was our recently completed second edition of the Valencian Summer School in Machine Learning celebrated last September 2016 in Spain. Over 140 attendees representing 53 companies and 21 academic organizations from 19 countries travelled to Valencia for a crash course in Machine Learning and it was a great success! What are the next steps? Encouraged by the level of interest and motivated by our mission to democratize Machine Learning, we continue spreading Machine Learning concepts with this series of courses.

SAP SE (NYSE: SAP) today announced three new courses delivered on the openSAP platform to guide users through the transformative effects experienced as a result of using iOS technology, machine learning and SAP S/4HANA to implement everyday business processes. These courses come in addition to the recent release of "Upgrade of Systems Based on SAP NetWeaver – Advanced Topics," which investigates the latest tools and features essential for SAP software system upgrades and maintenance. The new courses on openSAP will cover how the partnership between SAP and Apple is optimizing the use of iOS technology for end-to-end business processes. They also provide users an in-depth look at how machine learning is giving rise to new intelligent applications. SAP Fiori for iOS – An Introduction: The recent partnership forged between SAP and Apple will enable developers to build quickly their own native apps for Apple iOS devices.

BigML's Fall 2016 Release is here! Join us on Tuesday, November 29, at 10:00 AM PST (Portland, Oregon / GMT -08:00) / 07:00 PM CET (Valencia, Spain / GMT 01:00) for a FREE live webinar to get a first look at the latest version of BigML! We'll be focusing on Topic Models, the latest resource that helps you […] Source link

This is the hard skills development open hack session. Experts will be available to assist with a variety of things from Tensor Flow to Docker to Microsoft Cognitive Services and Azure. There are several projects in motion as well as folks taking several online classes. Come to learn about Python, Machine Learning, Robotics, how they work together and start getting some hands on experience. There will be pointers to guided tutorials as well as other experts.

Decision trees can suffer from high variance which makes their results fragile to the specific training data used. Building multiple models from samples of your training data, called bagging, can reduce this variance, but the trees are highly correlated. Random Forest is an extension of bagging that in addition to building trees based on multiple samples of your training data, it also constrains the features that can be used to build the trees, forcing trees to be different. This, in turn, can give a lift in performance. In this tutorial, you will discover how to implement the Random Forest algorithm from scratch in Python.

Azure Machine Learning is a fully managed predictive analytics service that you can use to create predictive models against your data in SQL Data Warehouse, and then publish as ready-to-consume web services. You can learn the basics of predictive analytics and machine learning by reading Introduction to Machine Learning on Azure. You can then learn how to create, train, score and test a machine learning model using the Create experiment tutorial. We will read data from Product table in the AdventureWorksDW database. Start a new experiment by clicking NEW at the bottom of the Machine Learning Studio window, select EXPERIMENT, and then select Blank Experiment.

Implementing Enterprise AI is a unique and limited edition course that is focussed on AI Engineering / AI for the Enterprise. The course is launched for the first time and has limited spaces. Created in partnership with H2O.ai, the course uses Open Source technology to work with AI use cases. Successful participants will receive a certificate of completion and also validation of their project from H2O.ai. To sign up or learn more, email info@futuretext.com The course targets developers and Architects who want to transition their career to Enterprise AI.

If you're keeping up with the rapid changes in the technology industry, you're seeing a bunch of terms thrown around as if they're interchangeable--but really, there are some pretty important distinctions. In this post, we're going to demystify the differences, and clarify the relationships, among these terms, especially artificial intelligence, machine learning, and self-driving cars. Let's begin with a simple model for how we'll approach this topic: Artificial intelligence is the broad field that covers all sorts of different initiatives and efforts to create machines that behave intelligently. What exactly it means to'behave intelligently' is a question best left for the philosophers and cognitive scientists, but for us, it refers to creating machines that do the highly complex things that only humans have previously been able to do. That means that AI is about creating machines that do more than just follow the commands that we give them. They can process input, make decisions, and take action.

SAP SE (NYSE: SAP) today introduced three initiatives to make its business applications more intelligent and empower its ecosystem to build machine learning (ML) applications for customers. Spanning its own solutions, partner programs and educational offerings, these programs will help accelerate ML adoption across SAP's global customer base. This announcement was made at the SAP TechEd conference, being held November 8-10, 2016, in Barcelona. First, SAP has unveiled new intelligent business applications. A new solution, "brand intelligence," is supposed to analyze brand exposure in video and images by leveraging deep learning.