Deep learning : a practitioner's approach /
Patterson, Josh,
Deep learning : a practitioner's approach / a practitioner's approach / Josh Patterson and Adam Gibson. - 507 p. color illustrations
Includes index.
A review of machine learning -- Foundations of neural networks and deep learning -- Fundamentals of deep networks -- Major architecture of deep networks -- Building deep networks -- Tuning deep networks -- Tuning specific deep network architectures -- Vectorization -- Using deep learning and DL4J on Spark -- What is artificial intelligence? -- RL4J and reinforcement learning -- Numbers everyone should know -- Neural networks and backpropagation: a mathematical approach -- Using the ND4J API -- Using DataVec -- Working with DL4J from source -- Setting up DL4J projects -- Setting up GPUs for DL4J projects -- Troubleshooting DL4J installations.
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.
9781491914250
AD209375-0802-482F-ABA4-D5EFC47F1E2C OverDrive, Inc. http://www.overdrive.com
Machine learning.
Artificial intelligence.
Neural networks (Computer science)
COMPUTERS--General.
Artificial intelligence.
Machine learning.
Neural networks (Computer science)
Electronic books.
QA325.5 / .P38 2017eb
006.31 PAT
Deep learning : a practitioner's approach / a practitioner's approach / Josh Patterson and Adam Gibson. - 507 p. color illustrations
Includes index.
A review of machine learning -- Foundations of neural networks and deep learning -- Fundamentals of deep networks -- Major architecture of deep networks -- Building deep networks -- Tuning deep networks -- Tuning specific deep network architectures -- Vectorization -- Using deep learning and DL4J on Spark -- What is artificial intelligence? -- RL4J and reinforcement learning -- Numbers everyone should know -- Neural networks and backpropagation: a mathematical approach -- Using the ND4J API -- Using DataVec -- Working with DL4J from source -- Setting up DL4J projects -- Setting up GPUs for DL4J projects -- Troubleshooting DL4J installations.
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.
9781491914250
AD209375-0802-482F-ABA4-D5EFC47F1E2C OverDrive, Inc. http://www.overdrive.com
Machine learning.
Artificial intelligence.
Neural networks (Computer science)
COMPUTERS--General.
Artificial intelligence.
Machine learning.
Neural networks (Computer science)
Electronic books.
QA325.5 / .P38 2017eb
006.31 PAT