Deep Learning A Practitioner's Approach

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.

- Chapter 1 - A Review of Machine
- Chapter 3 - Fundamentals of Deep Networks
- Chapter 4 - Major Architectures of Deep Networks
- Chapter 5 - Building Deep Network
- Chapter 6 - Tuning Deep Networks
- Chapter 7 - Tuning Specific Deep Network Architectures
- Chapter 8 - Vectorization
- Chapter 9 - Using Deep Learning and DL4J on Spark

Josh Patterson currently runs a consultancy in the big data machine learning / deep learning space. Previously Josh worked as a Principal Solutions Architect at Cloudera and as a machine learning / distributed systems engineer at the Tennessee Valley Authority where he broughtHadoop into the smart grid with the openPDC project.

Adam Gibson is a deep­-learning specialist based in San Francisco who works with Fortune 500 companies, hedge funds, PR firms and startup accelerators to create their machine-­learning projects.