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|
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|QA297.5 .Z53 1983 Finite elements and approximation /||QA300 .J825 2006 Analysis and Probability : Wavelets, Signals, Fractals /||QA320 .P434 1993 Spectral analysis for physical applications : multitaper and conventional univariate techniques /||QA325.5 .P38 2017 Deep learning : a practitioner's approach /||QA371 .Y36 2016 Uncertain differential equation.||QA374 .S6 2006 Elements of partial differential equations /||QA377 .F85 1997 Spectral elements for transport-dominated equations /|
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.