Looking for one central source where you can learn key findings on machine learning? Deep Learning: A Practitioner's Approach 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.
Table of Contents
Chapter 1 A Review of Machine Learning
Chapter 2 Foundations of Neural Networks
Chapter 3 Fundamentals of Deep Networks
Chapter 4 Major Architectures of Deep Networks
Chapter 5 Building Deep Networks
Chapter 6 Tuning Deep Networks
Chapter 7 Tuning Specific Deep Network Architectures
Chapter 8 Vectorization
Chapter 9 Using Deep Learning and DL4J on Spark