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020 _a9783031206399
024 7 _a10.1007/978-3-031-20639-9
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aQ325.5
_b2023 EB
100 1 _aSomani, Ayush
_eautor
_0(orcid)0000-0002-8614-6611
_1https://orcid.org/0000-0002-8614-6611
_4http://id.loc.gov/vocabulary/relators/aut
_9689466
245 1 0 _aInterpretability in Deep Learning
_cby Ayush Somani, Alexander Horsch, Dilip K. Prasad
250 _a1st ed 2023
264 1 _aCham
_bSpringer International Publishing
_c2023
300 _a1 recurso en línea
336 _atexto
_btxt
_2rdacontent
337 _aelectrónico
_bc
_2rdamedia
338 _arecurso electrónico
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChapter 1. Introduction -- Chapter 2. Neural networks for deep learning -- Chapter 3. Knowledge Encoding and Interpretation -- Chapter 4. Interpretation in Specific Deep Learning Architectures -- Chapter 5. Fuzzy Deep Learning.
520 _aThis book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of computer vision, optics and machine learning related topic. The book can be used as a monograph on interpretability in deep learning covering the most recent topics as well as a textbook for graduate students. Scientists with research, development and application responsibilities benefit from its systematic exposition. .
988 _aSpringer_Computer_2023
650 7 _2embne
_9166090
_aAprendizaje automático
700 1 _9689467
_aHorsch, Alexander
_eautor
700 1 _9689468
_aPrasad , Dilip K.
_eautor
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-20639-9
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
998 _b01/2024
_dz
_eb
_zSI