As the data world undergoes its Cambrian explosion phase, our data tools need to become more advanced to keep pace. Deep Learning has emerged as a key tool in the non-linear arms race of machine learning. In the video below Josh Patterson and Adam Gibson take a look at how we can parallelize Deep Belief Networks in Deep Learning on Hadoop’s next generation YARN framework with Iterative Reduce. They’ll also look at some real world examples of processing data with Deep Learning such as image classification and natural language processing. The talk was presented at the recent Hadoop Summit 2014 in San Jose.
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