There are so many deep learning libraries to choose from. Which are the good professional libraries that are worth learning and which are someones side project and should be avoided. It is hard to tell the difference. In this post you will discover the top deep learning libraries that you should consider learning and using in your own deep learning project. Popular Deep Learning Libraries Photo by Nikki, some rights reserved.

My name's Stelios and I work in Search Marketing on some of the biggest brands in Australia with Big Data Analytics requirements. Throughout 2015, I did a lot. I've very excited to start off 2016 with a blog post about some of the most exciting things I've ever seen throughout my short but eventful digital career: Big Data Analytics and Machine Learning algorithms. One of the biggest events in 2015 in my opinion was Google sharing to the public that for the past three months, search results received direct input from a machine learning, possibly deep learning algorithm. They further remarked it had returned more accurate search results than a Google Engineer, who up till 2015 could've told you what made a page rank in Google.

A blockchain intelligence vendor focused on combining the technology used to record and verify transactions with big data and artificial intelligence has attracted a pair of top technologist to serve in senior positions. Skry Inc., formerly Coinalytics, unveiled a name change this week along with the addition of new CTO and chief data scientist. The block chain analytics and intelligence firm based in Silicon Valley said Akash Singh, former CTO for data science at Chinese telecommunications giant Huawei (SHE: 002502) will serve as Skry's CTO. Singh also worked at IBM (NYSE: IBM), contributing to the development of its Watson cognitive computing platform. Also joining Skry is artificial intelligence researcher Masoud Nikravesh, former director of computational science and engineering at the University of California at Berkeley's Center for Information Technology Research in the Interest of Society.

The way we do, understand and organize work is about to change fundamentally. Technologies such as artificial intelligence and machine learning, robotics and 3D-printing are not only disrupting business models, but also revolutionizing the labor market. If 3D-printing enables anybody to produce whatever whenever - what does that mean for the future of manufacturing? If the gig economy grows further, will that eventually replace the 9-to-5 model? If smart machines learn to execute more and more cognitive tasks – does that steer us towards a post-work-society?

Mark Zuckerberg wants to overtake Elon Musk to become the real-world version of Marvel superhero Tony Stark. The billionaire Facebook founder has expressed his desire (in a Facebook post, of course) to spend 2016 building an artificially intelligent assistant to help run his life at home and work – and directly compared it to Jarvis, the AI companion developed by Stark in the Iron Man films. Previous aims have included spending a year eating only meat from animals he killed himself in 2011, to read two books a month in 2015, and to learn Mandarin in a year in 2010. And when he declares one of the challenges, he goes hard on it: in October last year, he showed off his language abilities, delivering a 20-minute speech to students at Beijing's Tsinghua University entirely in Mandarin. Zuckerberg will start the project by "exploring what technology is already out there". Existing home-automation tools from companies such as Google's Nest, Phillips and Samsung all allow a fairly high level of control of a "smart home", and can be paired with voice control software, including that from Apple, Amazon and Massachusetts-based specialists Nuance.

Imagine this: your hospital administrator asks you to help reduce the length of inpatient stays and they need a plan within a week. Chances are, most of you couldn't. But, the technology to mine and analyze your data does exist. Much like your daily Google searches, it's possible to input your search criteria, click Enter, and have answers at your fingertips in seconds. Doing so is part of radiology's push toward using big data, said Woojin Kim, MD, director of innovation at Montage Healthcare Solutions, Inc. "Radiology doesn't yet have big data like other industries, but that's changing rapidly. People want access to data to be able to turn insight into action," he said.

Driving at night is one of the most dangerous activities you can do in a car. Research from the federal government has shown that you're three times more likely to die in a nighttime car crash than in a daytime accident. That figure is no doubt heightened by the effects of drunken and drowsy driving -- but poor night vision can also play a role. That's why some car makers are trying to get their driverless vehicles to operate completely in the dark. In a recent test, Ford's engineers turned off the headlights, donned some night-vision goggles and sent their prototype Fusion sedan on a drive through the company's test track in Arizona.

The Journalism Department at Emerson College and the Emerson Engagement Lab recently invited William Li to give a talk to introduce machine learning to journalism and communications students. This is a live blog account of the talk by Catherine D'Ignazio. William Li is a 2015–2016 Fellow at the Harvard University Berkman Center for Internet and Society and a 2016 PhD computer science graduate from MIT. He develops and applies machine learning methods to answer social science questions computationally and to promote public understanding of law, politics, and public policy. His projects include predicting the authors of unsigned Supreme Court opinions, visualizing the complexity of our laws, and discovering ideas from large collections of public comments on proposed regulations. William has also worked on recommender systems, speech recognition, and user activity prediction at Apple and Mitsubishi Electric. He did his master's degrees at MIT in computer science and the Technology and Policy Program, founded the MIT Assistive Technology Club, and has taught classes that involve civic collaborations with organizations such as the Massachusetts Committee for Public Counsel Services, Greater Boston Legal Services, and the Cambridge Commission for People with Disabilities. William Li introduces the topic and that he wants to make the session very interactive.

After reading Pete Warden's excellent TensorFlow for Poets, I was impressed at how easy it seemed to build a working deep learning classifier. It was so simple that I had to try it myself. I have a lot of photos around, mostly of birds and butterflies. So, I decided to build a simple butterfly classifier. I chose butterflies because I didn't have as many photos to work with, and because they were already fairly well sorted.

In the hope of not letting decent data go to waste, even if it's only 19 rows, this post is about pulling useful information from a weird, tiny dataset that I collected during the summer of 2014: inter-arrival times of the G train, New York City's least respected and (possibly) most misunderstood subway line. The only non-shuttle service to not touch Manhattan, the G is known to suffer from relatively threadbare scheduled service. It even has fewer cars than other lines and as a result is much shorter, like it's yet to reach puberty. I've been totally reliant on it for years however, as have many other people who live in Brooklyn, and am very interested in the extent to which it actually does operate relative to its reported badness. This led me in part to spend a few weeks imputing G train inter-arrival times (i.e. the times between trains) onto my iPhone every morning during my commute.