PDF Download Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and Classification

PDF Download 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

PDF Download Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and Classification

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From the Back Cover

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.

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Product details

Hardcover: 282 pages

Publisher: Springer; 1st ed. 2017 edition (May 19, 2017)

Language: English

ISBN-10: 331957549X

ISBN-13: 978-3319575490

Product Dimensions:

6.5 x 0.9 x 9.6 inches

Shipping Weight: 1.2 pounds (View shipping rates and policies)

Average Customer Review:

2.0 out of 5 stars

1 customer review

Amazon Best Sellers Rank:

#2,531,515 in Books (See Top 100 in Books)

I have not had a chance to read the whole book. However, the book did have at least 2 errors in the CNN backprop chapter. That kinda sealed it for me. I returned.

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

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

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

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