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Bibliographic Details

Title

Deep Learning: A Practitioner's Approach

Publisher

O'Reilly Media

Publication Date

2017

Language

English

ISBN 10

1491914254

ISBN 13

9781491914250

Binding

Soft cover

Condition

New

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About this title

Synopsis

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

About the Author

Adam Gibson is a deep--learning specialist based in San Francisco who works with Fortune 500 companies, hedge funds, PR firms and startup accelerators to create their machine--learning projects. Adam has a strong track record helping companies handle and interpret big real-time data. Adam has been a computer nerd since he was 13, and actively contributes to the open--source community through deeplearning4j.org. Josh Patterson currently runs a consultancy in the big data machine learning / deep learning space. Previously Josh worked as a Principal Solutions Architect at Cloudera and as a machine learning / distributed systems engineer at the Tennessee Valley Authority where he brought Hadoop into the smart grid with the openPDC project. Josh has a Masters in Computer Science from the University of Tennessee at Chattanooga where he did published research on mesh networks (tinyOS) and social insect optimization algorithms. Josh has over 17 years in software development and is very active in the open source space contributing to projects such as deeplearning4j, Apache Mahout, Metronome, IterativeReduce, openPDC, and JMotif.