Details
Guide to Convolutional Neural Networks
A Practical Application to Traffic-Sign Detection and Classification|
55,92 € |
|
| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 17.05.2017 |
| ISBN/EAN: | 9783319575506 |
| Sprache: | englisch |
DRM-geschütztes eBook, Sie benötigen z.B. Adobe Digital Editions und eine Adobe ID zum Lesen.
Beschreibungen
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.
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
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 learningDiscusses the wide range of loss functions for training binary and multi-class classifiersIllustrates how to derive ConvNets from fully connected neural networks, and reviews different techniques for evaluating neural networksPresents a practical library for implementing ConvNets, explaining how to use a Python interface for the library to create and assess neural networksDescribes two real-world examples of the detection and classification of traffic signs using deep learning methodsExamines a range of varied techniques for visualizing neural networks, using a Python interfaceProvides 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.
Topics and features:
Explains the fundamental concepts behind training linear classifiers and feature learningDiscusses the wide range of loss functions for training binary and multi-class classifiersIllustrates how to derive ConvNets from fully connected neural networks, and reviews different techniques for evaluating neural networksPresents a practical library for implementing ConvNets, explaining how to use a Python interface for the library to create and assess neural networksDescribes two real-world examples of the detection and classification of traffic signs using deep learning methodsExamines a range of varied techniques for visualizing neural networks, using a Python interfaceProvides 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.
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?
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?
