A Practitioner's Approach
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 more » 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 « less