Deep Learning: A Practitioner's Approach Paperback – 2017 by Josh Patterson

Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning specially deep neural network 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, youíll 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, DL4Jís workflow tool
Learn how to use DL4J natively on Spark and Hadoop 
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Deep Learning: A Practitioner's Approach Paperback – 2017 by Josh Patterson

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Deep Learning: A Practitioner's Approach Paperback – 2017 by Josh Patterson