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Deep learning : a Practitioner's approach By Josh Patterson and Adam Gibson.

By: Material type: TextTextPublication details: Mumbai: Shroff publishers & distributors c2017.Edition: 1Description: i-xxi+507PISBN:
  • 9789352136049
Subject(s): DDC classification:
  • 006.31 PAT-D
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.
Summary: 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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Holdings
Item type Current library Home library Call number Status Date due Barcode
Book Book Dept. of Computational Biology and Bioinformatics Processing Center Dept. of Computational Biology and Bioinformatics 006.31 PAT-D (Browse shelf(Opens below)) Available DCB3972

3rd Indian reprint

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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