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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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イーブック ¥7,299
価格の適用国: Japan (日本円価格は個人のお客様のみ有効) (小計)
  • ISBN 978-3-319-57550-6
  • ウォーターマーク付、 DRMフリー
  • ファイル形式: PDF, EPUB
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  • ご購入後、すぐにダウンロードしていただけます。
ハードカバー ¥9,125
価格の適用国: Japan (日本円価格は個人のお客様のみ有効) (小計)
  • ISBN 978-3-319-57549-0
  • 個人のお客様には、世界中どこでも配送料無料でお届けします。
  • Usually dispatched within 3 to 5 business days.
ソフトカバー ¥9,125
価格の適用国: Japan (日本円価格は個人のお客様のみ有効) (小計)
  • ISBN 978-3-319-86190-6
  • 個人のお客様には、世界中どこでも配送料無料でお届けします。
  • Usually dispatched within 3 to 5 business days.
この書籍について

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)

書籍の購入

イーブック ¥7,299
価格の適用国: Japan (日本円価格は個人のお客様のみ有効) (小計)
  • ISBN 978-3-319-57550-6
  • ウォーターマーク付、 DRMフリー
  • ファイル形式: PDF, EPUB
  • どの電子書籍リーダーからでもすぐにお読みいただけます。
  • ご購入後、すぐにダウンロードしていただけます。
ハードカバー ¥9,125
価格の適用国: Japan (日本円価格は個人のお客様のみ有効) (小計)
  • ISBN 978-3-319-57549-0
  • 個人のお客様には、世界中どこでも配送料無料でお届けします。
  • Usually dispatched within 3 to 5 business days.
ソフトカバー ¥9,125
価格の適用国: Japan (日本円価格は個人のお客様のみ有効) (小計)
  • ISBN 978-3-319-86190-6
  • 個人のお客様には、世界中どこでも配送料無料でお届けします。
  • Usually dispatched within 3 to 5 business days.
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書誌情報

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
イーブック ISBN
978-3-319-57550-6
DOI
10.1007/978-3-319-57550-6
ハードカバー ISBN
978-3-319-57549-0
ソフトカバー 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