Although interest in machine learning has reached a high point, lofty expectations often scut tle projects before they get very far. How can machine learning—especially deep neural networks— maI‹e 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 networI‹s.
Authors Josh Patterson and Adam Gibson provide the fundamentals ofdeep 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 worI‹fIows. Through real- world examples, you‘ll learn methods and strategies for training deep network architectures and running deep learning worI‹flows on Sparl‹ and Hadoop with DL4J.
- Dive into machine learningconcepts in general, as well as deep learning in particular
- Understand how deep networI‹s evolved from neural network fundamentals
- Explore the major deep network architectures, including Convolutional and Recurrent
- Learn how to map specific deep networI‹s 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
