Deep Learning: A Practitioner's Approach

Deep Learning: A Practitioner's Approach

<div><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></div>