Although interest in machine learning has reached a high point,
lofty expectations often scuttle projects before they get very far.
How can machine learning-especially deep neural networks-make a
real difference in your organization? This hands-on guide not only
provides the most practical information available on the subject,
but also helps you get started building efficient deep learning
networks. Authors Adam Gibson and Josh Patterson provide theory on
deep learning before introducing their open-source Deeplearning4j
(DL4J) library for developing production-class workflows. Through
real-world examples, you'll learn methods and strategies for
training deep network architectures and running deep learning
workflows on Spark and Hadoop with DL4J. Dive into machine learning
concepts in general, as well as deep learning in particular
Understand how deep networks evolved from neural network
fundamentals Explore the major deep network architectures,
including Convolutional and Recurrent Learn how to map specific
deep networks to the right problem Walk through the fundamentals of
tuning general neural networks and specific deep network
architectures Use vectorization techniques for different data types
with DataVec, DL4J's workflow tool Learn how to use DL4J natively
on Spark and Hadoop
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