東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Deep learning for data analytics = f...

Das, Himansu,

Linked to FindBook

Google Book

Amazon

博客來

Deep learning for data analytics = foundations, biomedical applications, and challenges /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning for data analytics/ edited 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.).
[NT 15003449]:	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.
Subject:	Machine learning. -
Online resource:	https://www.sciencedirect.com/science/book/9780128197646
ISBN:	9780128226087 (electronic bk.)

Deep learning for data analytics = foundations, 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:

533906
Machine learning.
Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: Q325.5

Dewey Class. No.: 006.3/1

Deep learning for data analytics = foundations, biomedical applications, and challenges /
LDR:02661cmm a2200229 a 4500 001 2308392
006 m o d
007 cr |n|||||||||
008 230530s2020 enk ob 001 0 eng d
020 $a 9780128226087 (electronic bk.)
020 $a 0128226080 (electronic bk.)
020 $a 9780128197646
035 $a 022298135
040 $a Uk $b eng $c Uk
041 0 $a eng
050 4 $a Q325.5
082 0 4 $a 006.3/1 $2 23
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.
650 0 $a Machine learning. $3 533906
655 4 $a Electronic books. $2 lcsh $3 542853
700 1 $a Das, Himansu, $e editor. $3 3614666
700 1 $a Pradhan, Chittaranjan, $e editor. $3 3614829
700 1 $a Dey, Nilanjan $e editor. $3 3614830
856 4 0 $u https://www.sciencedirect.com/science/book/9780128197646