Deep learning : a practitioner's approach / Josh Patterson and Adam Gibson.
Material type: TextPublication details: Mumbai : Shroff Publishers & Distributors, 2018.Description: xxi, 507 p. : ill. ; 24 cmISBN:- 9789352136049
- 006.31 23 PAT
- QA325.5 .P38 2017
Item type | Current library | Call number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|
Books | Learning Resource Centre | 006.31 PAT (Browse shelf(Opens below)) | Available | 13865 |
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.
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.--
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