Deep Learning: A Practitioner's Approach
Language: English
Published by O'Reilly Media, 2017
- First Edition
- Softcover
- Used
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- Title
- Deep Learning: A Practitioner's Approach
- Author
- Patterson, Josh, Gibson, Adam
- Publisher
- O'Reilly Media
- Publication year
- 2017
- Condition
- Good
- Binding
- paperback
- Language
- English
- ISBN 10
- 1491914254
- ISBN 13
- 9781491914250
- Edition
- 1st Edition.
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
"Synopsis" may belong to another edition of this title.
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.
"About the title" may belong to another edition of this title.
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