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Sinopsis
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."Sinopsis" puede pertenecer a otra edición de este libro.
De la contraportada
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
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Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and Classification
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Guide to Convolutional Neural Networks : A Practical Application to Traffic-Sign Detection and Classification
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Springer-Verlag GmbH, 2017
ISBN 10: 331957549X
ISBN 13: 9783319575490
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Guide to Convolutional Neural Networks. A Practical Application to Traffic-Sign Detection and Classification.
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ISBN 10: 331957549X
ISBN 13: 9783319575490
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Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and Classification
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Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and Classification
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Guide to Convolutional Neural Networks
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Springer International Publishing, 2017
ISBN 10: 331957549X
ISBN 13: 9783319575490
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Describes how to practically solve problems of traffic sign detection and classification using deep learning methodsExplains how the methods can be easily implemented, without requiring prior background knowledge in the field of deep learning. Nº de ref. del artículo: 147656769
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Guide to Convolutional Neural Networks
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Springer International Publishing Mai 2017, 2017
ISBN 10: 331957549X
ISBN 13: 9783319575490
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 308 pp. Englisch. Nº de ref. del artículo: 9783319575490
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Guide to Convolutional Neural Networks : A Practical Application to Traffic-Sign Detection and Classification
Publicado por
Springer International Publishing, 2017
ISBN 10: 331957549X
ISBN 13: 9783319575490
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783319575490
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Guide to Convolutional Neural Networks : A Practical Application to Traffic-Sign Detection and Classification
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Guide to Convolutional Neural Networks : A Practical Application to Traffic-Sign Detection and Classification
Impresión bajo demandaLibrería: Majestic Books, Hounslow, Reino Unido
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Condición: New. Print on Demand. Nº de ref. del artículo: 369607972
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