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Deep learning for data analyticsfoundations, biomedical applications, and challenges /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning for data analyticsedited by Himansu Das, Chittaranjan Pradhan and Nilanjan Dey.
Reminder of title:	foundations, biomedical applications, and challenges /
other author:	Das, Himansu,
Published:	London :Academic Press,2020.
Description:	1 online resource (218 p.).
Subject:	Machine learning.
Online resource:	https://www.sciencedirect.com/science/book/9780128197646
ISBN:	9780128226087 (electronic bk.)

Deep learning for data analyticsfoundations, biomedical applications, and challenges /
Deep learning for data analyticsfoundations, biomedical applications, and challenges /[electronic resource] :edited by Himansu Das, Chittaranjan Pradhan and Nilanjan Dey. - London :Academic Press,2020. - 1 online resource (218 p.).

Includes bibliographical references and index.

Section I Deep Learning Basics and Mathematical Background 1. Introduction to Deep Learning 2. Probability and information Theory 3. Deep Learning Basics 4. Deep Architectures 5. Deep Auto-Encoders 6. Multilayer Perceptron 7. Artificial Neural Network 8. Deep Neural Network 9. Deep Belief Network 10. Recurrent Neural Networks 11. Convolutional Neural Networks 12. Restricted Boltzmann Machines Section II Deep Learning in Data Science 13. Data Analytics Basics 14. Enterprise Data Science 15. Predictive Analysis 16. Scalability of deep learning methods 17. Statistical learning for mining and analysis of big data 18. Computational Intelligence Methodology for Data Science 19. Optimization for deep learning (e.g. model structure optimization, large-scale optimization, hyper-parameter optimization, etc) 20. Feature selection using deep learning 21. Novel methodologies using deep learning for classification, detection and segmentation Section III Deep Learning in Engineering Applications 22. Deep Learning for Pattern Recognition 23. Deep Learning for Biomedical Engineering 24. Deep Learning for Image Processing 25. Deep Learning for Image Classification 26. Deep Learning for Medical Image Recognition 27. Deep learning for Remote Sensing image processing 28. Deep Learning for Image and Video Retrieval 29. Deep Learning for Visual Saliency 30. Deep Learning for Visual Understanding 31. Deep Learning for Visual Tracking 32. Deep Learning for Object Segmentation and Shape Models 33. Deep Learning for Object Detection and Recognition 34. Deep Learning for Human Actions Recognition 35. Deep Learning for Facial Recognition 36. Deep Learning for Scene Understanding 37. Deep Learning for Internet of Things 38. Deep Learning for Big Data Analytics 39. Deep Learning for Clinical and Health Informatics 40. Deep Learning foe Sentiment Analysis.

ISBN: 9780128226087 (electronic bk.)Subjects--Topical Terms:

188639
Machine learning.
Index Terms--Genre/Form:

214472
Electronic books.

LC Class. No.: Q325.5

Dewey Class. No.: 006.3/1

Deep learning for data analyticsfoundations, biomedical applications, and challenges /
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245 0 0 $a Deep learning for data analytics $h [electronic resource] : $b foundations, biomedical applications, and challenges / $c edited by Himansu Das, Chittaranjan Pradhan and Nilanjan Dey.
260 $a London : $b Academic Press, $c 2020.
300 $a 1 online resource (218 p.).
504 $a Includes bibliographical references and index.
505 0 $a Section I Deep Learning Basics and Mathematical Background 1. Introduction to Deep Learning 2. Probability and information Theory 3. Deep Learning Basics 4. Deep Architectures 5. Deep Auto-Encoders 6. Multilayer Perceptron 7. Artificial Neural Network 8. Deep Neural Network 9. Deep Belief Network 10. Recurrent Neural Networks 11. Convolutional Neural Networks 12. Restricted Boltzmann Machines Section II Deep Learning in Data Science 13. Data Analytics Basics 14. Enterprise Data Science 15. Predictive Analysis 16. Scalability of deep learning methods 17. Statistical learning for mining and analysis of big data 18. Computational Intelligence Methodology for Data Science 19. Optimization for deep learning (e.g. model structure optimization, large-scale optimization, hyper-parameter optimization, etc) 20. Feature selection using deep learning 21. Novel methodologies using deep learning for classification, detection and segmentation Section III Deep Learning in Engineering Applications 22. Deep Learning for Pattern Recognition 23. Deep Learning for Biomedical Engineering 24. Deep Learning for Image Processing 25. Deep Learning for Image Classification 26. Deep Learning for Medical Image Recognition 27. Deep learning for Remote Sensing image processing 28. Deep Learning for Image and Video Retrieval 29. Deep Learning for Visual Saliency 30. Deep Learning for Visual Understanding 31. Deep Learning for Visual Tracking 32. Deep Learning for Object Segmentation and Shape Models 33. Deep Learning for Object Detection and Recognition 34. Deep Learning for Human Actions Recognition 35. Deep Learning for Facial Recognition 36. Deep Learning for Scene Understanding 37. Deep Learning for Internet of Things 38. Deep Learning for Big Data Analytics 39. Deep Learning for Clinical and Health Informatics 40. Deep Learning foe Sentiment Analysis.
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655 4 $a Electronic books. $2 local. $3 214472
700 1 $a Das, Himansu, $e editor. $3 926535
700 1 $a Pradhan, Chittaranjan, $e editor. $3 926689
700 1 $a Dey, Nilanjan $e editor. $3 926690
856 4 0 $u https://www.sciencedirect.com/science/book/9780128197646