xxiii, 282 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband besto�en, daher M�ngelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.

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Bibliographic Details

Title

Guide to Convolutional Neural Networks. A Practical Application to Traffic-Sign Detection and Classification.

Publisher

Cham, Springer.

Publication Date

2017

Language

English

ISBN 10

331957549X

ISBN 13

9783319575490

Binding

Hardcover

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About this title

Synopsis

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.

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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

"About this title" may belong to another edition of this title.

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