November 2018
Intermediate to advanced
322 pages
7h 54m
English
Content preview from TensorFlow Machine Learning Projects
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Summary
Deep learning models provide better performance when the training dataset is large (big data). Training models for big data is computationally expensive. This problem can be handled using the divide and conquer approach: we divide the extensive computation part to many machines in a cluster, in other words, distributed AI.
One way of achieving this is by using Google's distributed TensorFlow, the API that helps in distributing the model training among different worker machines in the cluster. You need to specify the address of each worker machine and the parameter server. This makes the task of scaling the model difficult and cumbersome.
This problem can be solved by using the TensorFlowOnSpark API. By making minimal changes to the ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.