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Guide to Convolutional Neural Networks

A Practical Application to Traffic-Sign Detection and Classification

Authors: Habibi Aghdam, Hamed, Jahani Heravi, Elnaz

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  • Describes how to practically solve problems of traffic sign detection and classification using deep learning methods
  • Explains how the methods can be easily implemented, without requiring prior background knowledge in the field of deep learning
  • Discusses the theory behind deep learning and the relevant mathematical models, as well as illustrating how to implement a ConvNet in practice​
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eBook 53,54 €
price for Spain (gross)
  • ISBN 978-3-319-57550-6
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 67,59 €
price for Spain (gross)
  • ISBN 978-3-319-57549-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
Softcover 67,59 €
price for Spain (gross)
  • ISBN 978-3-319-86190-6
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

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.

Table of contents (7 chapters)

Table of contents (7 chapters)

Buy this book

eBook 53,54 €
price for Spain (gross)
  • ISBN 978-3-319-57550-6
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 67,59 €
price for Spain (gross)
  • ISBN 978-3-319-57549-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
Softcover 67,59 €
price for Spain (gross)
  • ISBN 978-3-319-86190-6
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Guide to Convolutional Neural Networks
Book Subtitle
A Practical Application to Traffic-Sign Detection and Classification
Authors
Copyright
2017
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing AG
eBook ISBN
978-3-319-57550-6
DOI
10.1007/978-3-319-57550-6
Hardcover ISBN
978-3-319-57549-0
Softcover ISBN
978-3-319-86190-6
Edition Number
1
Number of Pages
XXIII, 282
Number of Illustrations
39 b/w illustrations, 111 illustrations in colour
Topics