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Data science = theory, algorithms, a...

Verma, Gyanendra K.

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Data science = theory, algorithms, and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data science/ edited by Gyanendra K. Verma ... [et al.].
其他題名:	theory, algorithms, and applications /
其他作者:	Verma, Gyanendra K.
出版者:	Singapore :Springer Singapore : : 2021.,
面頁冊數:	xxvii, 437 p. :ill. (some col.), digital ;24 cm.
內容註:	A Deep Learning Technique for Real-Time Active Car Tracking with Quadrotor Luiz Gustavo Miranda Pinto, Wander Mendes Martins and Alexandre Carlos Brandao Ramos (Federal University of Itajuba - Unifei, Brazil) -- On fusion of NIR and VW information for cross-spectral iris matching Ritesh Vyas, Tirupathiraju Kanumuri, Gyanendra Sheoran and Pawan Dubey (National Institute of Technology Delhi, Delhi, India) -- Spark Enhanced Framework for Medical Phrase Embedding using Deep Neural Network Amol Bhopale and Ashish Tiwari (Visvesvaraya National Institute of Technology, Nagpur, India) -- Performance Analysis of big.LITTLE System with various Branch Prediction Schemes Froila Rodrigues and Nitesh Guinde (Goa College of Engineering, Farmagudi, Ponda, GoaIndia) -- Two-stage Credit Scoring Model based on Evolutionary Feature Selection and Ensemble Neural Networks Diwakar Tripathi, Damodar Reddy Edla, Annushri Bablani and Venkatanareshbabu Kuppili (Madanapalle Institute of Technology & Science, Madanapalle, A.P., India) -- Image Processing and Deep Learning for Drone Autonomous Indoor Flight Pedro Lucas de Brito, Wander Mendes Martins and Alexandre Carlos Brandao Ramos Federal University of Itajuba - Unifei, Brazil) -- Sensitivity Analysis of Multi Objective Fractional Programming using Genetic Algorithm Debasish Roy (IIT, Kharagpur, India)
Contained By:	Springer Nature eBook
標題:	Neural networks (Computer science) -
電子資源:	https://doi.org/10.1007/978-981-16-1681-5
ISBN:	9789811616815

Data science = theory, algorithms, and applications /
Data sciencetheory, algorithms, and applications /[electronic resource] :edited by Gyanendra K. Verma ... [et al.]. - Singapore :Springer Singapore :2021. - xxvii, 437 p. :ill. (some col.), digital ;24 cm. - Transactions on computer systems and networks,2730-7492. - Transactions on computer systems and networks..

A Deep Learning Technique for Real-Time Active Car Tracking with Quadrotor Luiz Gustavo Miranda Pinto, Wander Mendes Martins and Alexandre Carlos Brandao Ramos (Federal University of Itajuba - Unifei, Brazil) -- On fusion of NIR and VW information for cross-spectral iris matching Ritesh Vyas, Tirupathiraju Kanumuri, Gyanendra Sheoran and Pawan Dubey (National Institute of Technology Delhi, Delhi, India) -- Spark Enhanced Framework for Medical Phrase Embedding using Deep Neural Network Amol Bhopale and Ashish Tiwari (Visvesvaraya National Institute of Technology, Nagpur, India) -- Performance Analysis of big.LITTLE System with various Branch Prediction Schemes Froila Rodrigues and Nitesh Guinde (Goa College of Engineering, Farmagudi, Ponda, GoaIndia) -- Two-stage Credit Scoring Model based on Evolutionary Feature Selection and Ensemble Neural Networks Diwakar Tripathi, Damodar Reddy Edla, Annushri Bablani and Venkatanareshbabu Kuppili (Madanapalle Institute of Technology & Science, Madanapalle, A.P., India) -- Image Processing and Deep Learning for Drone Autonomous Indoor Flight Pedro Lucas de Brito, Wander Mendes Martins and Alexandre Carlos Brandao Ramos Federal University of Itajuba - Unifei, Brazil) -- Sensitivity Analysis of Multi Objective Fractional Programming using Genetic Algorithm Debasish Roy (IIT, Kharagpur, India)

This book targets an audience with a basic understanding of deep learning, its architectures, and its application in the multimedia domain. Background in machine learning is helpful in exploring various aspects of deep learning. Deep learning models have a major impact on multimedia research and raised the performance bar substantially in many of the standard evaluations. Moreover, new multi-modal challenges are tackled, which older systems would not have been able to handle. However, it is very difficult to comprehend, let alone guide, the process of learning in deep neural networks, there is an air of uncertainty about exactly what and how these networks learn. By the end of the book, the readers will have an understanding of different deep learning approaches, models, pre-trained models, and familiarity with the implementation of various deep learning algorithms using various frameworks and libraries.

ISBN: 9789811616815

Standard No.: 10.1007/978-981-16-1681-5doiSubjects--Topical Terms:

532070
Neural networks (Computer science)

LC Class. No.: QA76.87 / .D38 2021

Dewey Class. No.: 006.32

Data science = theory, algorithms, and applications /
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505 0 $a A Deep Learning Technique for Real-Time Active Car Tracking with Quadrotor Luiz Gustavo Miranda Pinto, Wander Mendes Martins and Alexandre Carlos Brandao Ramos (Federal University of Itajuba - Unifei, Brazil) -- On fusion of NIR and VW information for cross-spectral iris matching Ritesh Vyas, Tirupathiraju Kanumuri, Gyanendra Sheoran and Pawan Dubey (National Institute of Technology Delhi, Delhi, India) -- Spark Enhanced Framework for Medical Phrase Embedding using Deep Neural Network Amol Bhopale and Ashish Tiwari (Visvesvaraya National Institute of Technology, Nagpur, India) -- Performance Analysis of big.LITTLE System with various Branch Prediction Schemes Froila Rodrigues and Nitesh Guinde (Goa College of Engineering, Farmagudi, Ponda, GoaIndia) -- Two-stage Credit Scoring Model based on Evolutionary Feature Selection and Ensemble Neural Networks Diwakar Tripathi, Damodar Reddy Edla, Annushri Bablani and Venkatanareshbabu Kuppili (Madanapalle Institute of Technology & Science, Madanapalle, A.P., India) -- Image Processing and Deep Learning for Drone Autonomous Indoor Flight Pedro Lucas de Brito, Wander Mendes Martins and Alexandre Carlos Brandao Ramos Federal University of Itajuba - Unifei, Brazil) -- Sensitivity Analysis of Multi Objective Fractional Programming using Genetic Algorithm Debasish Roy (IIT, Kharagpur, India)
520 $a This book targets an audience with a basic understanding of deep learning, its architectures, and its application in the multimedia domain. Background in machine learning is helpful in exploring various aspects of deep learning. Deep learning models have a major impact on multimedia research and raised the performance bar substantially in many of the standard evaluations. Moreover, new multi-modal challenges are tackled, which older systems would not have been able to handle. However, it is very difficult to comprehend, let alone guide, the process of learning in deep neural networks, there is an air of uncertainty about exactly what and how these networks learn. By the end of the book, the readers will have an understanding of different deep learning approaches, models, pre-trained models, and familiarity with the implementation of various deep learning algorithms using various frameworks and libraries.
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