國立高雄大學圖資館 |

Language: English

Back

Computer vision using deep learningn...

SpringerLink (Online service)

Computer vision using deep learningneural network architectures with Python and Keras /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computer vision using deep learningby Vaibhav Verdhan.
Reminder of title:	neural network architectures with Python and Keras /
Author:	Verdhan, Vaibhav.
Published:	Berkeley, CA :Apress :2021.
Description:	xxi, 308 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Computer vision.
Online resource:	https://doi.org/10.1007/978-1-4842-6616-8
ISBN:	9781484266168$q(electronic bk.)

Computer vision using deep learningneural network architectures with Python and Keras /
Verdhan, Vaibhav.

Computer vision using deep learningneural network architectures with Python and Keras /[electronic resource] :by Vaibhav Verdhan. - Berkeley, CA :Apress :2021. - xxi, 308 p. :ill., digital ;24 cm.

Chapter 1 Introduction to Computer Vision and Deep Learning -- Chapter 2 Nuts and Bolts of Deep Learning for Computer Vision -- Chapter 3 Image Classification using LeNet -- Chapter 4 VGGNet and AlexNext Networks -- Chapter 5 Object Detection Using Deep Learning -- Chapter 6 Facial Recognition and Gesture Recognition -- Chapter 7 Video Analytics Using Deep Learning -- Chapter 8 End-to-end Model Development -- Appendix.

Organizations spend huge resources in developing software that can perform the way a human does. Image classification, object detection and tracking, pose estimation, facial recognition, and sentiment estimation all play a major role in solving computer vision problems. This book will bring into focus these and other deep learning architectures and techniques to help you create solutions using Keras and the TensorFlow library. You'll also review mutliple neural network architectures, including LeNet, AlexNet, VGG, Inception, R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, and SqueezeNet and see how they work alongside Python code via best practices, tips, tricks, shortcuts, and pitfalls. All code snippets will be broken down and discussed thoroughly so you can implement the same principles in your respective environments. Computer Vision Using Deep Learning offers a comprehensive yet succinct guide that stitches DL and CV together to automate operations, reduce human intervention, increase capability, and cut the costs. You will: Examine deep learning code and concepts to apply guiding principles to your own projects Classify and evaluate various architectures to better understand your options in various use cases Go behind the scenes of basic deep learning functions to find out how they work.

ISBN: 9781484266168$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-6616-8doiSubjects--Topical Terms:

200113
Computer vision.

LC Class. No.: TA1634

Dewey Class. No.: 006.37

Computer vision using deep learningneural network architectures with Python and Keras /
LDR:02761nmm a2200325 a 4500 001 600822
003 DE-He213
005 20210618135048.0
006 m d
007 cr nn 008maaau
008 211104s2021 cau s 0 eng d
020 $a 9781484266168$q(electronic bk.)
020 $a 9781484266151$q(paper)
024 7 $a 10.1007/978-1-4842-6616-8 $2 doi
035 $a 978-1-4842-6616-8
040 $a GP $c GP
041 0 $a eng
050 4 $a TA1634
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.37 $2 23
090 $a TA1634 $b .V483 2021
100 1 $a Verdhan, Vaibhav. $3 877829
245 1 0 $a Computer vision using deep learning $h [electronic resource] : $b neural network architectures with Python and Keras / $c by Vaibhav Verdhan.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2021.
300 $a xxi, 308 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1 Introduction to Computer Vision and Deep Learning -- Chapter 2 Nuts and Bolts of Deep Learning for Computer Vision -- Chapter 3 Image Classification using LeNet -- Chapter 4 VGGNet and AlexNext Networks -- Chapter 5 Object Detection Using Deep Learning -- Chapter 6 Facial Recognition and Gesture Recognition -- Chapter 7 Video Analytics Using Deep Learning -- Chapter 8 End-to-end Model Development -- Appendix.
520 $a Organizations spend huge resources in developing software that can perform the way a human does. Image classification, object detection and tracking, pose estimation, facial recognition, and sentiment estimation all play a major role in solving computer vision problems. This book will bring into focus these and other deep learning architectures and techniques to help you create solutions using Keras and the TensorFlow library. You'll also review mutliple neural network architectures, including LeNet, AlexNet, VGG, Inception, R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, and SqueezeNet and see how they work alongside Python code via best practices, tips, tricks, shortcuts, and pitfalls. All code snippets will be broken down and discussed thoroughly so you can implement the same principles in your respective environments. Computer Vision Using Deep Learning offers a comprehensive yet succinct guide that stitches DL and CV together to automate operations, reduce human intervention, increase capability, and cut the costs. You will: Examine deep learning code and concepts to apply guiding principles to your own projects Classify and evaluate various architectures to better understand your options in various use cases Go behind the scenes of basic deep learning functions to find out how they work.
650 0 $a Computer vision. $3 200113
650 0 $a Pattern recognition systems. $3 183725
650 1 4 $a Artificial Intelligence. $3 212515
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-6616-8
950 $a Professional and Applied Computing (SpringerNature-12059)