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Handbook of Deep Learning Applications

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Cover of 'Handbook of Deep Learning Applications'

Table of Contents

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    Book Overview
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    Chapter 1 Designing a Neural Network from Scratch for Big Data Powered by Multi-node GPUs
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    Chapter 2 Deep Learning for Scene Understanding
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    Chapter 3 An Application of Deep Learning in Character Recognition: An Overview
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    Chapter 4 Deep Learning for Driverless Vehicles
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    Chapter 5 Deep Learning for Document Representation
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    Chapter 6 Applications of Deep Learning in Medical Imaging
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    Chapter 7 Deep Learning for Marine Species Recognition
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    Chapter 8 Deep Molecular Representation in Cheminformatics
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    Chapter 9 A Brief Survey and an Application of Semantic Image Segmentation for Autonomous Driving
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    Chapter 10 Phase Identification and Workflow Modeling in Laparoscopy Surgeries Using Temporal Connectionism of Deep Visual Residual Abstractions
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    Chapter 11 Deep Learning Applications to Cytopathology: A Study on the Detection of Malaria and on the Classification of Leukaemia Cell-Lines
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    Chapter 12 Application of Deep Neural Networks for Disease Diagnosis Through Medical Data Sets
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    Chapter 13 Why Dose Layer-by-Layer Pre-training Improve Deep Neural Networks Learning?
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    Chapter 14 Springer: Deep Learning in eHealth
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    Chapter 15 Deep Learning for Brain Computer Interfaces
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    Chapter 16 Reducing Hierarchical Deep Learning Networks as Game Playing Artefact Using Regret Matching
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    Chapter 17 Deep Learning in Gene Expression Modeling
Attention for Chapter 17: Deep Learning in Gene Expression Modeling
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Chapter title
Deep Learning in Gene Expression Modeling
Chapter number 17
Book title
Handbook of Deep Learning Applications
Published by
Springer, Cham, January 2019
DOI 10.1007/978-3-030-11479-4_17
Book ISBNs
978-3-03-011478-7, 978-3-03-011479-4
Authors

Dinesh Kumar, Dharmendra Sharma, Kumar, Dinesh, Sharma, Dharmendra

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 31%
Student > Master 3 23%
Student > Bachelor 2 15%
Lecturer 1 8%
Professor > Associate Professor 1 8%
Other 0 0%
Unknown 2 15%
Readers by discipline Count As %
Computer Science 6 46%
Biochemistry, Genetics and Molecular Biology 2 15%
Social Sciences 1 8%
Materials Science 1 8%
Engineering 1 8%
Other 0 0%
Unknown 2 15%