Deep Learning - by Josh Patterson & Adam Gibson (Paperback)
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<p/><br></br><p><b> About the Book </b></p></br></br>How can machine learning--especially deep neural networks--make a real difference in your organization? This hands-on guide not only provides practical information, but helps you get started building efficient deep learning networks. The authors provide the fundamentals of deep learning--tuning, parallelization, vectorization, and building pipelines--that are valid for any library before introducing the 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.<p/><br></br><p><b> Book Synopsis </b></p></br></br><p>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.</p><p>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.</p><ul><li>Dive into machine learning concepts in general, as well as deep learning in particular</li><li>Understand how deep networks evolved from neural network fundamentals</li><li>Explore the major deep network architectures, including Convolutional and Recurrent</li><li>Learn how to map specific deep networks to the right problem</li><li>Walk through the fundamentals of tuning general neural networks and specific deep network architectures</li><li>Use vectorization techniques for different data types with DataVec, DL4J's workflow tool</li><li>Learn how to use DL4J natively on Spark and Hadoop</li></ul><p/><br></br><p><b> About the Author </b></p></br></br><p>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.</p><p>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.</p>
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