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
Josh Patterson is CEO of Patterson Consulting, a solution integrator at the intersection of big data and applied machine learning. In this role, he brings his unique perspective blending a decade of big data experience and wide-ranging deep learning experience to Fortune 500 projects. At the Tennessee Valley Authority (TVA), Josh drove the integration of Apache Hadoop for large-scale data storage and processing of smart grid phasor measurement unit (PMU) data. Post-TVA, Josh was a principal solutions architect for a young Hadoop startup named Cloudera (CLDR), as employee 34. After leaving Cloudera, Josh co-founded the Deeplearning4j project and co-wrote Deep Learning: A Practitioner's Approach (O'Reilly Media). Josh was also the VP of Field Engineering for Skymind.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.
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