Deep Learning: A Practitioner’s Approach

Deep Learning: A Practitioner’s Approach Front Cover
520 pages

Book Description

Looking for one central source where you can learn key findings on machine learning? Deep Learning: A Practitioner's Approach provides developers and data scientists with the most practical available on the subject, including deep learning , best practices, and use cases.
Authors Adam Gibson and Josh Patterson present the latest relevant papers and techniques in a non­academic manner, and implement the core in their DL4J library. If you work in the embedded, desktop, and /Hadoop spaces and really want to understand deep learning, this is your book.

Table of Contents

Chapter 1 A Review of Machine Learning
Chapter 2 Foundations of Neural
Chapter 3 of Deep Networks
Chapter 4 Major Architectures of Deep Networks
Chapter 5 Building Deep Networks
Chapter 6 Tuning Deep Networks
Chapter 7 Tuning Specific Deep Network Architectures
Chapter 8 Vectorization
Chapter 9 Using Deep Learning and DL4J on Spark

Book Details

  • Title: Deep Learning: A Practitioner’s Approach
  • Author: ,
  • Length: 520 pages
  • Edition: 1
  • Language: English
  • Publisher:
  • Publication Date: 2017-03-20
  • ISBN-13: 9781491914199
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