Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and ClassificationSpringer, 17.05.2017 - 282 Seiten 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. |
Inhalt
1 | |
15 | |
3 Convolutional Neural Networks | 84 |
4 Caffe Library | 131 |
5 Classification of Traffic Signs | 167 |
6 Detecting Traffic Signs | 235 |
7 Visualizing Neural Networks | 247 |
A Appendix Gradient Descend | 259 |
Glossary | 275 |
279 | |
Andere Ausgaben - Alle anzeigen
Guide to Convolutional Neural Networks: A Practical Application to Traffic ... Hamed Habibi Aghdam,Elnaz Jahani Heravi Keine Leseprobe verfügbar - 2018 |
Guide to Convolutional Neural Networks: A Practical Application to Traffic ... Hamed Habibi Aghdam,Elnaz Jahani Heravi Keine Leseprobe verfügbar - 2017 |
Häufige Begriffe und Wortgruppen
activation function Aghdam applied architecture backpropagation bottom BTSC BTSC dataset Caffe library Ciresan class label classification accuracy classification problem classification score classified correctly computational graph confusion matrix ConvNet convolution layer convolutional neural networks decision boundary denotes derivative dimensional ensemble equal equation evaluate feature maps feature vector feedforward neural network filters fully connected layer gradient descend algorithm GTSRB dataset hidden layer hinge loss implemented increases input image iterations learning rate linear classifier linear model linearly separable logistic loss loss function method mini-batch multiclass neuron node nonlinear number of parameters number of samples objective function optimization optional output overfit pictograph pixels plot pooling layer precision and recall Python random forest region ReLU activation Sermanet shows sigmoid sigmoid function softmax space stride technique test set text file traffic sign recognition traffic signs training samples training set validation set variable Visualizing weights zero