Deep learning : a Practitioner's approach By Josh Patterson and Adam Gibson.
Material type:![Text](/opac-tmpl/lib/famfamfam/BK.png)
- 9789352136049
- 006.31 PAT-D
Item type | Current library | Home library | Call number | Status | Date due | Barcode | |
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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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