Deep Learning
O'Reilly Media (Verlag)
978-1-4919-1425-0 (ISBN)
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
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. Adam has a strong track record helping companies handle and interpret big real-time data. Adam has been a computer nerd since he was 13, and actively contributes to the open--source community through deeplearning4j.org.
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 brought Hadoop into the smart grid with the openPDC project. Josh has a Masters in Computer Science from the University of Tennessee at Chattanooga where he did published research on mesh networks (tinyOS) and social insect optimization algorithms. Josh has over 17 years in software development and is very active in the open source space contributing to projects such as deeplearning4j, Apache Mahout, Metronome, IterativeReduce, openPDC, and JMotif.
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
| Erscheint lt. Verlag | 1.9.2017 |
|---|---|
| Verlagsort | Sebastopol |
| Sprache | englisch |
| Maße | 150 x 250 mm |
| Gewicht | 666 g |
| Einbandart | kartoniert |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Deep learning • machine learning • neural network • Neuronale Netze |
| ISBN-10 | 1-4919-1425-4 / 1491914254 |
| ISBN-13 | 978-1-4919-1425-0 / 9781491914250 |
| Zustand | Neuware |
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