Using deep learning to harness and explore large datasets has become increasingly important for businesses in every industry. There are many companies and services trying to make this a tenable problem, and yet, more people are still required to munge together home-grown solutions to meet their specific needs. Fortunately, there are many tools and resources in the market today that make building cognitive apps more doable. Here are nine interesting tools and resources I've seen and/or worked with recently to build cognitive apps: 1. Deeplearning.net: Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.

The Machine learning report evaluates technologies and applications in terms of their business impact, adoption rate and maturity level to help users decide where and when to invest. The Predictive Analytics Scores below – ordered on Forecasted Future Needs and Demand from High to Low – shows you Machine learning's Predictive Analysis. The link takes you to a corresponding product in The Art of Service's store to get started. The Art of Service's predictive model results enable businesses to discover and apply the most profitable technologies and applications, attracting the most profitable customers, and therefore helping maximize value from their investments. The Predictive Analytics algorithm evaluates and scores technologies and applications.

While the number of headlines about machine learning might lead one to think that we just discovered something profoundly new, the reality is that the technology is nearly as old as computing. It's no coincidence that Alan Turing, one of the most influential computer scientists of all time, started his 1950 treatise on computing with the question "Can machines think?" From our science fiction to our research labs, we have long questioned whether the creation of artificial versions of ourselves will somehow help us uncover the origin of our own consciousness, and more broadly, our role on earth. Unfortunately, the learning curve on AI is really damn steep. By tracing a bit of history, we should hopefully be able to get to the bottom of wtf machine learning really is.

Here are a few fields that are experiencing tremendous growth. The links below provide hundreds of popular articles recently published about these topics. Below is a chart from Google, about keyword popularity.

UPDATE: Mar 20, 2016 - Added my new follow-up course on Deep Learning, which covers ways to speed up and improve vanilla backpropagation: momentum and Nesterov momentum, adaptive learning rate algorithms like AdaGrad and RMSProp, utilizing the GPU on AWS EC2, and stochastic batch gradient descent. We look at TensorFlow and Theano starting from the basics - variables, functions, expressions, and simple optimizations - from there, building a neural network seems simple! Deep learning is all the rage these days. What exactly is deep learning? Well, it all boils down to neural networks.

They finally did it: They made "artificial intelligence" a buzzword. Typically, buzzwords don't come from decades-old evolving disciplines of computer science. Making "machine learning," "AI" and "neural nets" into buzzwords means that millions of developers are likely having their first experience with this stuff now. In that vein, we bring you a nice long list of machine learning, deep learning, neural network and artificial intelligence how-to's. Buzzword or not, it's fairly obvious this stuff will be a big part of enterprise software for the next few decades.

If there were a World Series of big data baseball analytics, Claudio Silva would be in the starting lineup. By tracking every movement of every player and the ball throughout the game, it's changed how coaches evaluate and train players and how fans watch the game. But Silva is swinging for the fences. He's now using GPU-accelerated deep learning to reveal minute details of player behavior and game patterns, which has the potential to revolutionize how coaches manage players and plan strategy. It could even give them the ability to make predictions about some aspects of the game.

Spookily powerful artificial intelligence (AI) systems may work so well because their structure exploits the fundamental laws of the universe, new research suggests. The new findings may help answer a longstanding mystery about a class of artificial intelligence that employ a strategy called deep learning. These deep learning or deep neural network programs, as they're called, are algorithms that have many layers in which lower-level calculations feed into higher ones. Deep neural networks often perform astonishingly well at solving problems as complex as beating the world's best player of the strategy board game Go or classifying cat photos, yet know one fully understood why. It turns out, one reason may be that they are tapping into the very special properties of the physical world, said Max Tegmark, a physicist at the Massachusetts Institute of Technology (MIT) and a co-author of the new research.

Unlike most other lists of top experts, this one is a hand-picked selection, not based on influence or Klout scores, or the number of Twitter followers and re-tweets, or other similar metrics. Each of these experts has his/her own Wikipedia page. Some might not even have a Twitter account. All of them have had a very strong academic and research career in the most prestigious places. Jeffrey Hawkins is the American founder of Palm Computing (where he invented the Palm Pilot) and Handspring (where he invented the Treo).

When it comes to machine learning frameworks, Google's Tensorflow is clearly the most popular option right now, but with CNTK, Microsoft also released its own internal framework at the beginning of the year. The company is launching the first beta of the next version (2.0) of CNTK today and with it, it hopes to challenge Tensorflow's leadership position. CNTK used to stand for'Computational Network Toolkit' but the software has now been renamed to Microsoft Cognitive Toolkit instead. Xuedong Huang, Microsoft's Chief Speech Scientist, told me that he believes CNTK/Cognitive Toolkit has always had plenty of advantages over Tensorflow and similar frameworks -- especially with regards to performance. According to Microsoft's benchmarks, Cognitive Toolkit continues to outperform its competitors in most tests and unsurprisingly, this new version is faster than the previous releases, especially when working on big data sets.