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Machine learning paradigms = advance...

Tsihrintzis, George A.

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Machine learning paradigms = advances in deep learning-based technological applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning paradigms/ edited by George A. Tsihrintzis, Lakhmi C. Jain.
其他題名:	advances in deep learning-based technological applications /
其他作者:	Tsihrintzis, George A.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xii, 430 p. :ill., digital ;24 cm.
內容註:	Chapter 1: Introduction to Deep Learning-based Technological Applications -- Chapter 2: Vision to Language: Methods, Metrics and Datasets -- Chapter 3: Deep Learning Techniques for Geospatial Data Analysis -- Chapter 4: Deep Learning Approaches in Food Recognition -- Chapter 5: Deep Learning for Twitter Sentiment Analysis: the Effect of pre-trained Word Embedding -- Chapter 6: A Good Defense is a Strong DNN: Defending the IoT with Deep Neural Networks -- Chapter 7: Survey on Deep Learning Techniques for Medical Imaging Application Area -- Chapter 8: Deep Learning Methods in Electroencephalography.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-3-030-49724-8
ISBN:	9783030497248

Machine learning paradigms = advances in deep learning-based technological applications /
Machine learning paradigmsadvances in deep learning-based technological applications /[electronic resource] :edited by George A. Tsihrintzis, Lakhmi C. Jain. - Cham :Springer International Publishing :2020. - xii, 430 p. :ill., digital ;24 cm. - Learning and analytics in intelligent systems,v.182662-3447 ;. - Learning and analytics in intelligent systems ;v.18..

Chapter 1: Introduction to Deep Learning-based Technological Applications -- Chapter 2: Vision to Language: Methods, Metrics and Datasets -- Chapter 3: Deep Learning Techniques for Geospatial Data Analysis -- Chapter 4: Deep Learning Approaches in Food Recognition -- Chapter 5: Deep Learning for Twitter Sentiment Analysis: the Effect of pre-trained Word Embedding -- Chapter 6: A Good Defense is a Strong DNN: Defending the IoT with Deep Neural Networks -- Chapter 7: Survey on Deep Learning Techniques for Medical Imaging Application Area -- Chapter 8: Deep Learning Methods in Electroencephalography.

At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.

ISBN: 9783030497248

Standard No.: 10.1007/978-3-030-49724-8doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .M334 2020

Dewey Class. No.: 006.31

Machine learning paradigms = advances in deep learning-based technological applications /
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520 $a At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.
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