Deep learning : a practitioner's approach / Josh Patterson and Adam Gibson.
Contributor(s): Gibson, Adam [author.].Publisher: Boston : O'Reilly, 2017Edition: First edition.Description: xxi, 507 pages : illustrations ; 24 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 1491914254; 9781491914250.Subject(s): Machine learning | Neural networks (Computer science) | Open source software
|Item type||Current location||Call number||Status||Date due||Item holds|
|BOOK||Mesa Lab||QA325.5 .P38 2017 (Browse shelf)||Checked out||02/18/2018|
Includes bibliographical references and 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.