Machine learning is one of the fastest-growing and most exciting fields out there, and deep learning represents its true bleeding edge. In this course, you'll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets. We'll show you how to train and optimize basic neural networks, convolutional neural networks, and long short term memory networks. Complete learning systems in TensorFlow will be introduced via projects and assignments. You will learn to solve new classes of problems that were once thought prohibitively challenging, and come to better appreciate the complex nature of human intelligence as you solve these same problems effortlessly using deep learning methods.

The title speaks much for itself. We are entering an era in computational technology, that is making what seemed like the wildest fantasies 10-15 years ago, actually becoming reality. More and more of the tedious and hard manual labor work of checking, analyzing and confirming numbers in computations and systems is being taken over by computers and machines, that can do this at an efficiency surpassing that of humans by tenfolds. But what does this mean for the future of analysis and work done by machines? No matter how quick, precise and superior computers and machines become over humans, there are still certain tasks that humans will be able to do better, due to our ability to improvise and be creative. For now, those are attributes that a computer or machine cannot learn to the same extent as a human.

NIPS 2015 was bigger than ever, literally: at circa 3700 attendees this was roughly twice as many attendees as last year, which in turn was roughly twice as many as the previous year. This is clearly unsustainable, but given the frenzied level of vendor and recruiting activities, perhaps there is room to grow. The main conference is single track, however, and already 3 days long: so even more action is moving to the poster sessions, which along with the workshops creates the feel of a diverse collection of smaller conferences. Obviously, my view of the action will be highly incomplete and biased towards my own interests. Reinforcement learning continues to ascend, extending the enthusiasm and energy from ICML.

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Comet Labs has assembled a coalition of investors and corporate partners to help accelerate product and customer development cycles for fast growing robotics and machine learning companies. Startups are paired with experienced mentors and are given access to industry partners and platform technology. The fund's partners include the world's largest global manufacturing, agriculture, and healthcare companies. About Comet Labs Comet Labs is an early-stage venture capital firm associated with a 300M fund, Legend Star. They work with startups developing solutions in specific industry verticals, as well as those building enabling technologies.

The technologies included within the HfS Intelligent Automation Continuum are evolving at a rapid rate today as are the market positionings of the various sofwtare vendors. To account for these changes, HfS has evolved the Continuum to reflect the increased confluence of Robotic Process Automation and Cognitive Computing and the role that Service Orchestration platforms can play in building comprehensive solutions for the automation of IT and business processes. This POV outlines the new Intelligent Automation Continuum and explains the reasons for the changes that have been made.

Sentient's mission is to transform how businesses tackle their most complex, mission critical problems by empowering them to make the right decisions faster. Sentient's technology has patented evolutionary and perceptual capabilities that will provide customers with highly sophisticated solutions, powered by the largest compute grid dedicated to distributed artificial intelligence. For more information, visit www.sentient.ai Utilizing evolutionary computation and deep learning – designed to continuously evolve and improve – Sentient aims to create the world's most powerful intelligent system. This system enables researchers, innovators and companies to solve mission- critical, high value, problems.

Andrew Moore was a professor of computer science and robotics at Carnegie Mellon University for a dozen years when Google hired him away in 2006 to lead some of its efforts around ad targeting and fraud prevention. CMU lured Moore back in 2014, making him the dean of its computer science school. But he still understands well what goes through his colleagues' minds when industry comes calling, and he says the battle to keep them in academia grows fiercer by the year. Earlier today, we talked with Moore about Uber, which famously raided the school's robotics department a year ago, poaching 40 of its researchers and scientists. We also talked about how Moore entices people to stay, and the newest new thing his 2,000-student school is focused on right now.

A robot in the home is usually seen as the stuff of sci-fi movies. But robots are increasingly coming closer as smart technology allows people to control the functions of their home from afar. As technology becomes more advanced, it's clear that the world is changing and there's a good chance that robots will be functioning in ordinary people's homes within the next decade or so. But what's less considered is just how at-home robots will change the way people live. In this guide, this issue is going to be examined.

You'll soon be getting much better video recommendations from YouTube. Google is launching new iOS and Android apps for YouTube aimed not only at a design refresh, but also at offering you smarter video recommendations. You'll also notice larger photos, which should help in identifying videos from particular channels. "The new recommendation system is based on deep neural network technology, which means it can find patterns automatically and keep learning and improving as it goes," said a blog post detailing the update. "Every day, we recommend hundreds of millions of different videos on Home, billions of times, in 76 languages."