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Deep Machine Learning: a Panel

Speaker  Andrew Ng, Honglak Lee, Ruslan Salakhutdinov, and Yoshua Bengio

Affiliation  University of Montreal, University of Michigan – Ann Arbor, Stanford University, University of Toronto

Host  Li Deng, Microsoft Research, and John Platt, Microsoft Research

Duration  01:55:37

Date recorded  16 July 2013

This panel session of the 2013 Microsoft Research Faculty Summit looks at deep learning, a sub-field of machine learning that focuses on hierarchical representations of features or concepts, where high-level semantic-like features can emerge via automatic layer-by-layer learning from low-level features.

In recent years, deep learning has achieved important successes in a variety of applied artificial intelligence tasks including speech recognition, computer vision, and natural language processing. The implications of such recent work have been prominently covered in recent media with both enthusiasm and skepticism. Since 2009, in partnership with academics, Microsoft Research has been pursuing deep learning research and technology transfer, and has pioneered the development of industry-scale deep learning technology for speech recognition. It is useful to share our experience with wider academic communities and learn from each other. To make the material and directions of interest to a broader computer science audience, we offer a tutorial to demystify the “black-art” label often attached to deep learning.

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