Read Deep Learning: A Practitioner's Approach

Deep Learning: A Practitioner's Approach

Josh Patterson

4.9/5 (27121 ratings)

Description: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 particularUnderstand how deep networks evolved from neural network fundamentalsExplore the major deep network architectures, including Convolutional and RecurrentLearn how to map specific deep networks to the right problemWalk through the fundamentals of tuning general neural networks and specific deep network architecturesUse vectorization techniques for different data types with DataVec, DL4J's workflow toolLearn how to use DL4J natively on Spark and HadoopWe have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Deep Learning: A Practitioner's Approach. To get started finding Deep Learning: A Practitioner's Approach, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

Pages: 532

Format: PDF, EPUB & Kindle Edition

Publisher: O'Reilly Media

Release: 2017

ISBN: 1491914211

Description: 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 particularUnderstand how deep networks evolved from neural network fundamentalsExplore the major deep network architectures, including Convolutional and RecurrentLearn how to map specific deep networks to the right problemWalk through the fundamentals of tuning general neural networks and specific deep network architecturesUse vectorization techniques for different data types with DataVec, DL4J's workflow toolLearn how to use DL4J natively on Spark and HadoopWe have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Deep Learning: A Practitioner's Approach. To get started finding Deep Learning: A Practitioner's Approach, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Deep Learning: A Practitioner's Approach

Deep Learning: A Practitioner's Approach

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