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Neural Networks across Space and Time
Recorded at:

| by Dave Snowdon Follow 0 Followers on Jun 29, 2018 |
41:29

Summary
Dave Snowdon starts with a brief introduction to deep neural networks, why they are important and how they work. He covers two of the most important deep neural architectures: convolutional networks which excel at handling images, and recurrent networks which handle time-series or sequential input. He shows examples of both convolutional and recurrent networks using the deeplearning4j framework.

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Bio

Dave Snowdon is a programmer working on cloud management of virtual desktop infrastructure (VDI) at VMware. He wrote a system to generate unique pieces of music for each user based on information extracted from audio, rhythms and images supplied by the user. More recently, he has been devoting his time to understanding ML with a particular emphasis on deep neural networks.

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