Distributed Deep Learning with Will Constable

http://traffic.libsyn.com/sedaily/Distributeddeeplearning.mp3Podcast: Play in new window | Download Deep learning allows engineers to build models that can make decisions based on training data. These models improve over time using stochastic gradient descent. When a model gets big enough, the training must be broken up across multiple machines. Two strategies for doing this are “model parallelism” which divides the model across machines and “data parallelism” which divides the data across multiple copies

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