We consider the problem of building highlevel, class-specific feature detectors from only unlabeled data. Authors: Quoc V. Le, Marc'Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S. Corrado, Jeffrey Dean and Andrew Y. Ng. (2012) Andrew Ng Adam Coates Brody Huval Quoc Le Andrew Maas Andrew Saxe Richard Socher Sameep Tandon Tao Wang Description Machine learning is a very successful technology but applying it today often requires spending substantial effort hand-designing features. This is true for applications in vision, audio and text. To address this, Ng's group and others are working on "deep learning" algorithms, which can automatically learn feature representations (often from unlabeled data) thus avoiding a lot of time-consuming engineering. These algorithms are based on building massive artificial neural networks that were loosely inspired by cortical (brain) computations.

Nando de Freitas 76,136 views Machine learning is not the future - Google I/O 2016 - Duration: 39:00.

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Deep learning is impacting everything from healthcare to transportation to manufacturing, and more. Companies are turning to deep learning to solve hard problems, like speech recognition, object recognition, and machine translation. One of the most impressive achievements this year was AlphaGo beating the best Go player in the world. With the victory, Go joins checkers, chess, othello, and Jeopardy as games machines have defeated human at. While beating someone at a board game might not seem useful on the surface, this is a huge deal.