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Guide to convolutional neural networ...

Habibi Aghdam, Hamed.

Guide to convolutional neural networksa practical application to traffic-sign detection and classification /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Guide to convolutional neural networksby Hamed Habibi Aghdam, Elnaz Jahani Heravi.
其他題名:	a practical application to traffic-sign detection and classification /
作者:	Habibi Aghdam, Hamed.
其他作者:	Heravi, Elnaz Jahani.
出版者:	Cham :Springer International Publishing :2017.
面頁冊數:	xxiii, 282 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Neural networks (Computer science)
電子資源:	http://dx.doi.org/10.1007/978-3-319-57550-6
ISBN:	9783319575506$q(electronic bk.)

Guide to convolutional neural networksa practical application to traffic-sign detection and classification /
Habibi Aghdam, Hamed.

Guide to convolutional neural networksa practical application to traffic-sign detection and classification /[electronic resource] :by Hamed Habibi Aghdam, Elnaz Jahani Heravi. - Cham :Springer International Publishing :2017. - xxiii, 282 p. :ill. (some col.), digital ;24 cm.

Traffic Sign Detection and Recognition -- Pattern Classification -- Convolutional Neural Networks -- Caffe Library -- Classification of Traffic Signs -- Detecting Traffic Signs -- Visualizing Neural Networks -- Appendix A: Gradient Descend.

This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification. The work presents techniques for optimizing the computational efficiency of ConvNets, as well as visualization techniques to better understand the underlying processes. The proposed models are also thoroughly evaluated from different perspectives, using exploratory and quantitative analysis. Topics and features: Explains the fundamental concepts behind training linear classifiers and feature learning Discusses the wide range of loss functions for training binary and multi-class classifiers Illustrates how to derive ConvNets from fully connected neural networks, and reviews different techniques for evaluating neural networks Presents a practical library for implementing ConvNets, explaining how to use a Python interface for the library to create and assess neural networks Describes two real-world examples of the detection and classification of traffic signs using deep learning methods Examines a range of varied techniques for visualizing neural networks, using a Python interface Provides self-study exercises at the end of each chapter, in addition to a helpful glossary, with relevant Python scripts supplied at an associated website This self-contained guide will benefit those who seek to both understand the theory behind deep learning, and to gain hands-on experience in implementing ConvNets in practice. As no prior background knowledge in the field is required to follow the material, the book is ideal for all students of computer vision and machine learning, and will also be of great interest to practitioners working on autonomous cars and advanced driver assistance systems.

ISBN: 9783319575506$q(electronic bk.)

Standard No.: 10.1007/978-3-319-57550-6doiSubjects--Topical Terms:

181982
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

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