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008 170518s2017 gw | s |||| 0|eng d
020 _a9783319575506
_9978-3-319-57550-6
024 7 _a10.1007/978-3-319-57550-6
_2doi
040 _cCUS
050 4 _aQ337.5
050 4 _aTK7882.P3
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_2bicssc
072 7 _aCOM016000
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072 7 _aUYQP
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082 0 4 _a006.4
_223
100 1 _aHabibi Aghdam, Hamed.
_eauthor.
_0(orcid)0000-0002-4881-9694
_1https://orcid.org/0000-0002-4881-9694
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aGuide to Convolutional Neural Networks
_h[electronic resource] :
_bA Practical Application to Traffic-Sign Detection and Classification /
_cby Hamed Habibi Aghdam, Elnaz Jahani Heravi.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXXIII, 282 p. 150 illus., 111 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aTraffic 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.
520 _aThis 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.
650 0 _aPattern recognition.
650 0 _aApplication software.
650 0 _aComputer organization.
650 0 _aSignal processing.
650 0 _aImage processing.
650 0 _aSpeech processing systems.
650 0 _aNatural language processing (Computer science).
650 0 _aAutomotive engineering.
650 1 4 _aPattern Recognition.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I2203X
650 2 4 _aInformation Systems Applications (incl. Internet).
_0https://scigraph.springernature.com/ontologies/product-market-codes/I18040
650 2 4 _aComputer Systems Organization and Communication Networks.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I13006
650 2 4 _aSignal, Image and Speech Processing.
_0https://scigraph.springernature.com/ontologies/product-market-codes/T24051
650 2 4 _aNatural Language Processing (NLP).
_0https://scigraph.springernature.com/ontologies/product-market-codes/I21040
650 2 4 _aAutomotive Engineering.
_0https://scigraph.springernature.com/ontologies/product-market-codes/T17047
700 1 _aJahani Heravi, Elnaz.
856 4 0 _uhttps://doi.org/10.1007/978-3-319-57550-6
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cEBK
999 _c205445
_d205445