Deep learning : 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 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.. How Can Machine Learning--especially Deep Neural Networks--make A Real Difference In Your Organization? This Hands-on Guide Not Only Provides Practical Information, But Helps You Get Started Building Efficient Deep Learning Networks. The Authors Provide The Fundamentals Of Deep 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 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.-- Looking for one central source where you can learn key findings on machine learning? Deep Learning: The Definitive Guide provides developers and data scientists with the most practical information available on the subject, including deep learning theory, best practices, and use cases. Authors Adam Gibson and Josh Patterson present the latest relevant papers and techniques in a nonacademic manner, and implement the core mathematics in their DL4J library. If you work in the embedded, desktop, and big data/Hadoop spaces and really want to understand deep learning, this is your book.