Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they even get very far. How can machine learning, especially deep nural networks, make a real difference in your organization? This hands-on guide note only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks.
This book provides the fundamentals of deep learning (tuning, paralleization, vectoriztion, 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 Haddop with DL4J.