This book sets out to show how machine learning, especially deep neural networks, can make a real difference. The hands-on guide provides practical information and 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.

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The book uses real-world examples to illustrate methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.

Author: Josh Patterson and Adam Gibson
Publisher: O'Reilly
Date: August 2017
Pages: 536
ISBN: 978-1491914250
Print: 1491914254
Kindle: B074D5YF1D
Audience: Java engineers and practicing data scientists
Level: Intermediate
Category: Artificial Intelligence

• 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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