Let's get started with lesson three of our Practicing Deep Learning Series. So far our focus has been on a very simple network comprised of a single neuron. Though we've discussed its parts, we have neglected to show it actually doing anything. The focus of part three is to start diving into some actual code to illustrate the simple network we've discussed. We will spend a fair amount of time on the single neuron network so that you can get familiar with Keras while gaining an understanding of the basics of a simple network. As soon as this is complete, we will be moving onto multilayer networks, which are much more powerful than the simple networks below.

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.

Stitch Fix values the input of both human experts and computer algorithms in our styling process. As we've pointed out before, this approach has a lot of benefits and so it's no surprise that more and more technologies (like Tesla's self-driving cars, Facebook's chat bot, and Wise.io's augmented customer service) are also marrying computer and human workforces. Interest has been rising in how to optimize this type of hybrid algorithm. At Stitch Fix we have realized that well-trained humans are just as important for this as well-trained machines. There are similarities and differences between training humans and computers.

If you think chatbots are hot right now -- with how they're being used in psychotherapy, turning into racist trolls, and presenting an existential threat to Apple -- just wait until they turn into full-fledged personal assistants. In five years time, digital personal assistants will even more important than your smartphone, says University of Washington computer scientist Pedro Domingos, author of "The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World." "What you have right now on your smartphone is dozens of apps," Domingos tells Tech Insider, "with each app doing it's own thing." On any given Friday night, you use one app to find a restaurant, another to buy a movie ticket, another to figure out how to get to where you're going, and another to find a date to take out with you. "It's incredibly annoying," he says, since the apps "don't talk to each other and you have to learn all these different interfaces."

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.

Advanced cyber-learning environments that involve virtual reality and artificial intelligence innovations are becoming powerful tools that can facilitate the explorations and conversations needed to solve society's challenges,

It's day one of F8, Facebook's annual developer conference, and the keynote is about to begin. We're expecting to hear about chatbots, more developer tools, live video and, of course, more about virtual reality. It's hard to imagine that, for most of us, the platform is barely 10 years old; it was opened to anyone who had a valid email address in September 2006. But, even beyond the realities of a digital and social world, in which we collectively create and share millions of pieces of content daily, we are now living in an age in which artificial intelligence and virtual reality are real, and are becoming even more embedded in our everyday lives. "Today we're going to do something different. We're going to walk through our roadmap for the next 10 years," says Zuckerberg. "Before we get into detail, I want to talk about our mission, and why the work we're doing together is more important than it's ever been. Facebook's mission is to connect the world, and the Internet has enabled us to access and share more information and ideas than ever before."