Catalyzing Deep Learning’s Impact in the Enterprise

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Deep learning is in the early stages of unlocking tremendous economic value outside its impact in large technology companies. While the algorithms have revolutionized consumer experiences in domains as varied as speech interfaces, image search, language translation and game AI, enterprises are still in the early stages with efforts to apply these algorithms to other areas - such as improving automotive speech interfaces, agricultural robotics, finding anomalies in IoT data, and more.

Individual data scientists can draw from several open source frameworks and basic hardware resources during the very initial investigative phases but quickly require significant hardware and software resources to build and deploy production models. The team at Nervana Systems (recently acquired by Intel) aims to change this, and have built a deep learning platform to make it easy for data scientists to start from the iterative, investigatory phase and take models all the way to deployment.

At the 2016 Deep Learning Summit in London, Arjun Bansal, Co-founder and VP of Algorithms at Nervana, presented Catalyzing Deep Learning’s Impact in the Enterprise. View the presentation with slides below.

View more video presentations and interviews from the 2016 Deep Learning Summit in London on the RE•WORK video hub.

The next Deep Learning Summit takes place in San Francisco on 26-27 January! Speakers include Ilya Sutskever, Research Director at OpenAI; Lise Getoor, Professor of Computer Science at UC Santa Cruz; Naveen Rao, CEO & Co-Founder of Nervana Systems; Danny Lange, Head of Machine Learning at Uber; and Brendan Frey, Co-founder & CEO of Deep Genomics. Book your pass here.

See the full events list here for events focused on AI, Deep Learning and Machine Intelligence taking place in London, Amsterdam, Boston, San Francisco, New York, Hong Kong and Singapore. 

Machine Learning Neural Networks Deep Learning Hardware AI Deep Learning Summit Machine Intelligence Deep Learning Algorithms


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