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Deep learning : a practitioner's approach
By: Patterson, Josh.
Contributor(s): Gibson, Adam.
Material type:
BookPublisher: 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.
| Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
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IIITD General Stacks | Computer Science and Engineering | 006.31 PAT-D (Browse shelf) | Available | 008723 | ||
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Books
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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: General Stacks , Collection code: Computer Science and Engineering Close shelf browser
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| 006.31 MIT-M Machine learning | 006.31 MIT-M Machine learning | 006.31 MIT-M Machine learning | 006.31 PAT-D Deep learning : | 006.31 RAM-T TensorFlow for deep learning : | 006.31 SHW-U Understanding machine learning : | 006.31 SHW-U Understanding machine learning : |
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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