建國科技大學圖書館 |

語系: 繁體中文

說明(常見問題)

圖書館個人資料蒐集告知聲明

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Computer vision using deep learning ...

Verdhan, Vaibhav.

Computer vision using deep learning : neural network architectures with Python and Keras

紀錄類型:	書目-語言資料,印刷品 : 單行本
副題名:	neural network architectures with Python and Keras
作者:	VerdhanVaibhav.,
出版地:	[Berkeley, CA]
出版者:	Apress;
出版年:	c2021.
面頁冊數:	xxi, 308 p.col. ill. : 24 cm.;
標題:	Computer vision. -
標題:	Pattern recognition systems. -
摘要註:	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:	9781484266151
內容註:	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.

Computer vision using deep learning : neural network architectures with Python and Keras
Verdhan, Vaibhav.

Computer vision using deep learning : neural network architectures with Python and Keras / Vaibhav Verdhan. - [Berkeley, CA] : Apress, c2021.. - xxi, 308 p. ; col. ill. ; 24 cm..
Chapter 1 Introduction to Computer Vision and Deep Learning.
Includes bibliographical references and index..
ISBN 9781484266151ISBN 1484266153
Computer vision.Pattern recognition systems.

Computer vision using deep learning : neural network architectures with Python and Keras
LDR:02440cam a2200241 450 001 396465
009 1237861959
010 1 $a 9781484266151 $b pbk. $d NT1113
010 1 $a 1484266153 $b pbk.
010 1 $z 1484266161 $b ebk.
010 1 $z 9781484266168 $b ebk.
100 $a 20211120d2021 k y0engy50 b
101 0 $a eng
102 $a us $b ca
105 $a a a 001yy
200 1 $a Computer vision using deep learning $e neural network architectures with Python and Keras $f Vaibhav Verdhan.
210 $a [Berkeley, CA] $c Apress $d c2021.
215 1 $a xxi, 308 p. $c col. ill. $d 24 cm.
320 $a Includes bibliographical references and index.
327 1 $a Chapter 1 Introduction to Computer Vision and Deep Learning $a Chapter 2 Nuts and Bolts of Deep Learning for Computer Vision $a Chapter 3 Image Classification using LeNet $a Chapter 4 VGGNet and AlexNext Networks $a Chapter 5 Object Detection Using Deep Learning $a Chapter 6 Facial Recognition and Gesture Recognition $a Chapter 7 Video Analytics Using Deep Learning $a Chapter 8 End-to-end Model Development $a Appendix.
330 $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.
606 $a Computer vision. $2 lc $3 27610
606 $a Pattern recognition systems. $2 lc $3 32209
676 $a 006.3/7 $v 23
680 $a TA1634
700 1 $a Verdhan $b Vaibhav. $3 389129
801 0 $a cw $b CTU $c 20211120 $g AACR2