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Deep learning in data analytics = re...

Acharjya, Debi Prasanna.

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Deep learning in data analytics = recent techniques, practices and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning in data analytics/ edited by Debi Prasanna Acharjya, Anirban Mitra, Noor Zaman.
其他題名:	recent techniques, practices and applications /
其他作者:	Acharjya, Debi Prasanna.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	xx, 266 p. :ill., digital ;24 cm.
內容註:	Study on Discrete Action Sequences using Deep Emotional Intelligence -- A Novel Noise Removal Technique Influenced by Deep Convolutional Autoencoders on Mammograms -- A High Security Framework through Human Brain using Algo Mixture Model Deep Learning Algorithm -- Knowledge Framework for Deep Learning: Congenital Heart Disease -- Computing System and Machine Learning -- Automatic Image Segmentation by Ranking based SVM in Convolutional Neural Network on Diabetic Fundus Image.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-3-030-75855-4
ISBN:	9783030758554

Deep learning in data analytics = recent techniques, practices and applications /
Deep learning in data analyticsrecent techniques, practices and applications /[electronic resource] :edited by Debi Prasanna Acharjya, Anirban Mitra, Noor Zaman. - Cham :Springer International Publishing :2022. - xx, 266 p. :ill., digital ;24 cm. - Studies in big data,v. 912197-6511 ;. - Studies in big data ;v. 91..

Study on Discrete Action Sequences using Deep Emotional Intelligence -- A Novel Noise Removal Technique Influenced by Deep Convolutional Autoencoders on Mammograms -- A High Security Framework through Human Brain using Algo Mixture Model Deep Learning Algorithm -- Knowledge Framework for Deep Learning: Congenital Heart Disease -- Computing System and Machine Learning -- Automatic Image Segmentation by Ranking based SVM in Convolutional Neural Network on Diabetic Fundus Image.

This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society. Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.

ISBN: 9783030758554

Standard No.: 10.1007/978-3-030-75855-4doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .D44 2022

Dewey Class. No.: 006.31

Deep learning in data analytics = recent techniques, practices and applications /
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520 $a This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society. Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.
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