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 applications.. Volume 3

Wani, M. Arif.

Linked to FindBook

Google Book

Amazon

博客來

Deep learning applications.. Volume 3

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning applications./ edited by M. Arif Wani ... [et al.].
other author:	Wani, M. Arif.
Published:	Singapore :Springer Singapore : : 2022.,
Description:	xii, 322 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Deep Rapid Class Augmentation; a New Progressive Learning Approach that Eliminates the Issue of Catastrophic Forgetting -- A Comprehensive Analysis of Subword Contextual Embeddings for Languages with Rich Morphology -- RGB and Depth Image Fusion for Object Detection using Deep Learning -- Dimension Estimation Using Autoencoders with Applications to Financial Market Analysis -- A New Clustering-Based Technique for the Acceleration of Deep Convolutional Networks -- Deep Learning based Time Series Forecasting -- DEAL: Deep Evidential Active Learning for Image Classification -- LB-CNN: Convolutional Neural Network with Latent Binarization for Large Scale Multi[1]class Classification -- Efficient Deployment of Deep Learning Models on Autonomous Robots in the ROS Environment -- Building Power Grid 2.0: Deep Learning and Federated Computations for Energy Decarbonization and Edge Resilience -- Improving the Donor Journey with Convolutional and Recurrent Neural Networks.
Contained By:	Springer Nature eBook
Subject:	Machine learning - Congresses. -
Online resource:	https://doi.org/10.1007/978-981-16-3357-7
ISBN:	9789811633577

Deep learning applications.. Volume 3
Deep learning applications.Volume 3[electronic resource] /edited by M. Arif Wani ... [et al.]. - Singapore :Springer Singapore :2022. - xii, 322 p. :ill. (some col.), digital ;24 cm. - Advances in intelligent systems and computing,v. 13952194-5365 ;. - Advances in intelligent systems and computing ;v. 1395..

Deep Rapid Class Augmentation; a New Progressive Learning Approach that Eliminates the Issue of Catastrophic Forgetting -- A Comprehensive Analysis of Subword Contextual Embeddings for Languages with Rich Morphology -- RGB and Depth Image Fusion for Object Detection using Deep Learning -- Dimension Estimation Using Autoencoders with Applications to Financial Market Analysis -- A New Clustering-Based Technique for the Acceleration of Deep Convolutional Networks -- Deep Learning based Time Series Forecasting -- DEAL: Deep Evidential Active Learning for Image Classification -- LB-CNN: Convolutional Neural Network with Latent Binarization for Large Scale Multi[1]class Classification -- Efficient Deployment of Deep Learning Models on Autonomous Robots in the ROS Environment -- Building Power Grid 2.0: Deep Learning and Federated Computations for Energy Decarbonization and Edge Resilience -- Improving the Donor Journey with Convolutional and Recurrent Neural Networks.

This book presents a compilation of extended version of selected papers from the 19th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2020) and focuses on deep learning networks in applications such as pneumonia detection in chest X-ray images, object detection and classification, RGB and depth image fusion, NLP tasks, dimensionality estimation, time series forecasting, building electric power grid for controllable energy resources, guiding charities in maximizing donations, and robotic control in industrial environments. Novel ways of using convolutional neural networks, recurrent neural network, autoencoder, deep evidential active learning, deep rapid class augmentation techniques, BERT models, multi-task learning networks, model compression and acceleration techniques, and conditional Feature Augmented and Transformed GAN (cFAT-GAN) for the above applications are covered in this book. Readers will find insights to help them realize novel ways of using deep learning architectures and algorithms in real-world applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and innovative product developers.

ISBN: 9789811633577

Standard No.: 10.1007/978-981-16-3357-7doiSubjects--Topical Terms:

576368
Machine learning
--Congresses.

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

Dewey Class. No.: 006.31

Deep learning applications.. Volume 3
LDR:03311nmm a2200337 a 4500 001 2296298
003 DE-He213
005 20211112080207.0
006 m d
007 cr nn 008maaau
008 230324s2022 si s 0 eng d
020 $a 9789811633577 $q (electronic bk.)
020 $a 9789811633560 $q (paper)
024 7 $a 10.1007/978-981-16-3357-7 $2 doi
035 $a 978-981-16-3357-7
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .D44 2022
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .D311 2022
245 0 0 $a Deep learning applications. $n Volume 3 $h [electronic resource] / $c edited by M. Arif Wani ... [et al.].
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2022.
300 $a xii, 322 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Advances in intelligent systems and computing, $x 2194-5365 ; $v v. 1395
505 0 $a Deep Rapid Class Augmentation; a New Progressive Learning Approach that Eliminates the Issue of Catastrophic Forgetting -- A Comprehensive Analysis of Subword Contextual Embeddings for Languages with Rich Morphology -- RGB and Depth Image Fusion for Object Detection using Deep Learning -- Dimension Estimation Using Autoencoders with Applications to Financial Market Analysis -- A New Clustering-Based Technique for the Acceleration of Deep Convolutional Networks -- Deep Learning based Time Series Forecasting -- DEAL: Deep Evidential Active Learning for Image Classification -- LB-CNN: Convolutional Neural Network with Latent Binarization for Large Scale Multi[1]class Classification -- Efficient Deployment of Deep Learning Models on Autonomous Robots in the ROS Environment -- Building Power Grid 2.0: Deep Learning and Federated Computations for Energy Decarbonization and Edge Resilience -- Improving the Donor Journey with Convolutional and Recurrent Neural Networks.
520 $a This book presents a compilation of extended version of selected papers from the 19th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2020) and focuses on deep learning networks in applications such as pneumonia detection in chest X-ray images, object detection and classification, RGB and depth image fusion, NLP tasks, dimensionality estimation, time series forecasting, building electric power grid for controllable energy resources, guiding charities in maximizing donations, and robotic control in industrial environments. Novel ways of using convolutional neural networks, recurrent neural network, autoencoder, deep evidential active learning, deep rapid class augmentation techniques, BERT models, multi-task learning networks, model compression and acceleration techniques, and conditional Feature Augmented and Transformed GAN (cFAT-GAN) for the above applications are covered in this book. Readers will find insights to help them realize novel ways of using deep learning architectures and algorithms in real-world applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and innovative product developers.
650 0 $a Machine learning $x Congresses. $3 576368
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 1619875
700 1 $a Wani, M. Arif. $3 3442826
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Advances in intelligent systems and computing ; $v v. 1395. $3 3590851
856 4 0 $u https://doi.org/10.1007/978-981-16-3357-7
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)