None of deep learning models discussed here work as classification algorithms. Instead, they can be seen as Pretrainin, automated feature selection and learning, creating a hierarchy of features etc. Once trained (features are selected), the input vectors are transformed into a better representation and these are in turn passed on to a real classifier such as SVM or Logistic regression. This can be represented as below.

Whether it's Google's headline-grabbing DeepMind AlphaGo victory, or Apple's weaving of "using deep neural network technology" into iOS 10, deep learning and artificial intelligence are all the rage these days, promising to take applications to new heights in how they interact with us mere mortals. To go deeper (yes, I went there) on the subject, I reached out to the team at the deep learning-focused company Skymind, creators of Deep Learning For Java (DL4J), and authors of the recently released O'Reilly book Deep Learning: A Practitioner's Approach, Josh Patterson and Adam Gibson. Josh and Adam offer us a gentle introduction to the subject in this interview, as well as insight into how they are building an open source-based business around deep learning. For the uninitiated, what is deep learning (DL) and why should I care about it? Adam Gibson (AG): Deep learning is just another term for neural networks, a set of algorithms that have been around for decades. For a long time people were skeptical about them, but as chips got more powerful and as we gathered more data to train them on, deep neural nets started breaking records. We're hitting expert human accuracy on a lot of problem sets, with accuracy rates in the high 90s, which is a quantum leap over other algorithms. So if you have a problem that matters to your business, you can probably attach a dollar value to that improvement in accuracy, and if you're a large business, that value can be huge. It's a competitive edge with a big impact on margins.Josh Patterson (JP): To build on what Adam said, with deep learning we're moving from manual feature creation to automated feature learning. The trick with deep learning is to recognize the input data type and match it to the correct deep network architecture to enable robust automated feature learning. An example is how automatically learn the features in complex image data, where historically this was harder for other machine learning methods. What problems are DL best suited for?

Tech pundit Tim O'Reilly had just tried the new Google Photos app, and he was amazed by the depth of its artificial intelligence. O'Reilly was standing a few feet from Google CEO and co-founder Larry Page this past May, at a small cocktail reception for the press at the annual Google I/O conference--the centerpiece of the company's year. Google had unveiled its personal photos app earlier in the day, and O'Reilly marveled that if he typed something like "gravestone" into the search box, the app could find a photo of his uncle's grave, taken so long ago. Google is open sourcing software that sits at the heart of its empire. The app uses an increasingly powerful form of artificial intelligence called deep learning. By analyzing thousands of photos of gravestones, this AI technology can learn to identify a gravestone it has never seen before.

Guest blog post by Christopher Dole and other contributors, originally posted here. Deep Learning is one of the most revolutionary and disruptive technologies ever developed in Data Science. Essentially, this is a class of algorithms inspired by how the human brain works, and it has the ability to automate and replace most of the world's jobs. This is what enables self-driving cars to function and what allows Spotify to create very customized playlists and recommendations. This is how YouTube is able to identify faces and animals in videos and how Siri can understand and process free speech in milliseconds.

Right on cue, this past week heralded in an announcement of OpenAI, a new non-profit started by a number of tech luminaries to spearhead AI research that is publicly accessible. The motivation is that apparently these scions of capitalism lose faith in Adam Smith's invisible hand when it comes to AI R&D. Musk continues to promote the idea that AI will be humanity's largest existential threat. Challenging this view, the HBR asks if "OpenAI [is] Solving the Wrong Problem", pointing to the implied lack of trust in capitalism. This is similar to my own parry: that the biggest existential threat to humanity is humanity.

Update: Though Yoshua Bengio originally referred to his incubator as Element.AI, the official name is now Element AI.

The Microsoft Cognitive Toolkit--previously known as CNTK--empowers you to harness the intelligence within massive datasets through deep learning by providing uncompromised scaling, speed and accuracy with commercial-grade quality and compatibility with the programming languages and algorithms you already use. Hear about the team that developed the Cognitive Toolkit, or read more below.

Deep learning is sometimes referred to as the intersection between machine learning and artificial intelligence. It is about designing algorithms that can make robots intelligent, such a face recognition techniques used in drones to detect and target terrorists, or pattern recognition / computer vision algorithms to automatically pilot a plane, a train, a boat or a car. Many deep learning algorithms (clustering, pattern recognition, automated bidding, recommendation engine, and so on) -- even though they appear in new contexts such as IoT or machine to machine communication -- still rely on relatively old-fashioned techniques such as logistic regression, SVM, decision trees, K-NN, naive Bayes, Bayesian modeling, ensembles, random forests, signal processing, filtering, graph theory, gaming theory, and many others. Some are new, such as indexation algorithms to automate digital publishing, improve search engines, or create and manage large catalogs such as Amazon's product listing. As a result, many deep learning practitioners call themselves data scientist, computer scientist, statistician, or sometimes engineer.

As if quantum computing wasn't mind-bending enough, one of D-Wave Systems' founders is now pursuing another futuristic idea: using artificial intelligence and high-tech exoskeleton suits to allow humans--and, at least according to one description of the technology, monkeys--to control and train an army of intelligent robots. Geordie Rose is a cofounder and chief technology officer of D-Wave, the Canadian company selling machines that it claims exploit quantum mechanical effects to solve certain problems hundreds of millions times faster than traditional computers. Now an IEEE Spectrum investigation has discovered that Rose is also CEO of Kindred Systems (aka Kindred AI), a stealthy startup he founded with others in 2014 dedicated to delivering advanced teleoperated and autonomous robots. The goal is making programming robots faster and less costly–and possibly revolutionize the world of work. Kindred has so far received well over US $10 million in funding, according to Data Collective, the venture capital firm that led one of the rounds.

Deep data science is a branch of data science that has little if any overlap with closely related fields such as machine learning, computer science, operations research, mathematics, or statistics. Even classical machine learning and statistical techniques such as clustering, density estimation, or tests of hypotheses, have model-free, data-driven, robust versions designed for automated processing (as in machine-to-machine communications), and thus these techniques also belong to deep data science. Note that unlike deep learning, deep data science is not the intersection of data science and artificial intelligence; however, the analogy between deep data science and deep learning is not completely meaningless, in the sense that both deal with automation. For a robust regression that will work even if all the traditional model assumptions are violated, click here. It is simple (it can be implemented in Excel and it is model-free), efficient and very comparable to the standard regression (when the model assumptions are not violated).