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Deep learning : a practitioner's approach

By: Patterson, Josh.
Contributor(s): Gibson, Adam.
Material type: materialTypeLabelBookPublisher: New Delhi : O'Reilly, ©2017Description: xxi, 507 p.; 25 cm.ISBN: 9789352136049.Subject(s): Machine learning | Artificial intelligence | Neural networks (Computer science) | COMPUTERS / General | Artificial intelligence | Machine learning | Neural networks (Computer science)Genre/Form: Electronic books.Online resources: Access eNYPL
Contents:
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.
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Item type Current location Collection Call number Status Date due Barcode Item holds
Books Books IIITD
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Computer Science and Engineering 006.31 PAT-D (Browse shelf) Checked out 18/10/2019 008723
Books Books IIITD
Reference
Computer Science and Engineering REF 006.31 PAT-D (Browse shelf) Not For Loan 008722
Total holds: 0
Browsing IIITD Shelves , Shelving location: Reference , Collection code: Computer Science and Engineering Close shelf browser
REF 006.31 MIT-M Machine learning REF 006.31 MOH-F Foundations of machine learning REF 006.31 MUR-M Machine learning : REF 006.31 PAT-D Deep learning : REF 006.31 SCH-B Boosting : REF 006.31 SCH-L Learning with kernels : REF 006.31 SHA-K Kernel methods for pattern analysis

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.

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