For an upcoming project we will be experimenting with Deep Learning approaches for NLP in an Azure environment (Amazon and Local are not an option right now). Azure offers NC6 (K80) and NV6 (M60) instances, but due to region restrictions it might be that only the M60 will be available. "In addition to the NC-Series, focused on compute, the NV-Series is focused more on visualization" Can anyone confirm that the M60 is appropriate for Deep Learning?

Your next doctor could very well be a bot. And bots, or automated programs, are likely to play a key role in finding cures for some of the most difficult-to-treat diseases and conditions. Artificial intelligence is rapidly moving into health care, led by some of the biggest technology companies and emerging startups using it to diagnose and respond to a raft of conditions. While technology has always played a role in medical care, a wave of investment from Silicon Valley and a flood of data from connected devices appear to be spurring innovation. "I think a tipping point was when Apple released its Research Kit," said Forrester Research analyst Kate McCarthy, referring to a program letting Apple users enable data from their daily activities to be used in medical studies.

The challenge is in creating a deep learning model to detect forms of malware that don't yet exist. In current machine learning research, accuracy estimates don't consider how systems will process future data. "If researchers forget to focus on sensitivity testing and time decay, our models are liable to fail catastrophically in the wild," she explains. This analysis will include a deep learning model designed to detect malicious URLs, which was trained and tested using three sources of URL data.

Bidirectional LSTMs are an extension of traditional LSTMs that can improve model performance on sequence classification problems. In problems where all timesteps of the input sequence are available, Bidirectional LSTMs train two instead of one LSTMs on the input sequence. The first on the input sequence as-is and the second on a reversed copy of the input sequence. This can provide additional context to the network and result in faster and even fuller learning on the problem. In this tutorial, you will discover how to develop Bidirectional LSTMs for sequence classification in Python with the Keras deep learning library.

Researchers fed the robot nearly 5,000 complete songs -- from Beethoven to the Beatles to Lady Gaga to Miles Davis -- and more than 2 million motifs, riffs and licks of music. Aside from giving the machine a seed, or the first four measures to use as a starting point, no humans are involved in either the composition or the performance of the music. The first two compositions are roughly 30 seconds in length. Ph.D. student Mason Bretan is the man behind the machine. He's worked with Shimon for seven years, enabling it to "listen" to music played by humans and improvise over pre-composed chord progressions.

Machine learning is trying to one-up just-in-time inventory with what can only be called before-it's-time inventory. The Economist reports that German online merchant Otto is using algorithms to predict what you'll order a week before you order it, reducing surplus stock and speeding deliveries: A deep-learning algorithm, which was originally designed for particle-physics experiments at the CERN laboratory in Geneva, does the heavy lifting. It analyses around 3bn past transactions and 200 variables (such as past sales, searches on Otto's site and weather information) to predict what customers will buy a week before they order. The AI system has proved so reliable--it predicts with 90% accuracy what will be sold within 30 days--that Otto allows it automatically to purchase around 200,000 items a month from third-party brands with no human intervention. It would be impossible for a person to scrutinise the variety of products, colours and sizes that the machine orders. Online retailing is a natural place for machine-learning technology, notes Nathan Benaich, an investor in AI.

Soljačić says that many researchers over the years have made claims about optics-based computers, but that "people dramatically over-promised, and it backfired." While many proposed uses of such photonic computers turned out not to be practical, a light-based neural-network system developed by this team "may be applicable for deep-learning for some applications," he says. Traditional computer architectures are not very efficient when it comes to the kinds of calculations needed for certain important neural-network tasks. Such tasks typically involve repeated multiplications of matrices, which can be very computationally intensive in conventional CPU or GPU chips. After years of research, the MIT team has come up with a way of performing these operations optically instead.

In the age of Amazon domination, competing retail interests need to get smarter and do so quickly. Such was the message at a recent Cowen panel discussion that highlighted how artificial intelligence and its more studious cousin, artificial intelligence / deep learning / machine learning, operate to build and maintain a retail business. The discussion featured three advanced technology providers, each of whom operated at varying stages of the retail cycle. At the top of the list is Foursquare, a firm that is popularly considered for its social media platform that allows people to communicate their location to an audience waiting with bated breath. Foursquare is more than an application that breaks news when John Q. Rando in Peoria, Illinois checks in at Applebee's, there is a technology underneath the application that provides useful intelligence.

This is an exciting time for those of us in computer vision -- we're seeing it merge with AI to enable all kinds of new possibilities. AI needs data with which to learn and process, and as we move closer to more "human"-like AI, it will increasingly need visual data. "This is one of the reasons all the major companies are at war to own the visual data of our activities," said LDV Capital's Evan Nisselson. "To do that, they need to own the camera." Amazon recently added a camera to its Alexa-powered Echo, for example, and Google (Lens) and Facebook recently made new recent augmented reality announcements.

It is hyperbole to say deep learning is achieving state-of-the-art results across a range of difficult problem domains. There is a lot of excitement around artificial intelligence, machine learning and deep learning at the moment. It is also an amazing opportunity to get on on the ground floor of some really powerful tech. I try hard to convince friends, colleagues and students to get started in deep learning and bold statements like the above are not enough. It requires stories, pictures and research papers.