000 | 03181nam a22005295i 4500 | ||
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001 | 978-3-319-75304-1 | ||
003 | DE-He213 | ||
005 | 20210201191509.0 | ||
007 | cr nn 008mamaa | ||
008 | 180322s2018 gw | s |||| 0|eng d | ||
020 |
_a9783319753041 _9978-3-319-75304-1 |
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050 | 4 | _aQ334-342 | |
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_a006.3 _223 |
100 | 1 |
_aCaterini, Anthony L. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
245 | 1 | 0 |
_aDeep Neural Networks in a Mathematical Framework _h[electronic resource] / _cby Anthony L. Caterini, Dong Eui Chang. |
250 | _a1st ed. 2018. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2018. |
|
300 |
_aXIII, 84 p. _bonline resource. |
||
336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aSpringerBriefs in Computer Science, _x2191-5768 |
|
500 | _aAcceso multiusuario | ||
520 | _aThis SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive gradient descent algorithms in a unified way for several neural network structures, including multilayer perceptrons, convolutional neural networks, deep autoencoders and recurrent neural networks. Furthermore, the authors developed framework is both more concise and mathematically intuitive than previous representations of neural networks. This SpringerBrief is one step towards unlocking the black box of Deep Learning. The authors believe that this framework will help catalyze further discoveries regarding the mathematical properties of neural networks.This SpringerBrief is accessible not only to researchers, professionals and students working and studying in the field of deep learning, but also to those outside of the neutral network community. | ||
541 |
_fUABC ; _cTemporal ; _d01/01/2021-12/31/2023. |
||
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aPattern recognition. | |
650 | 1 | 4 |
_aArtificial Intelligence. _0https://scigraph.springernature.com/ontologies/product-market-codes/I21000 |
650 | 2 | 4 |
_aPattern Recognition. _0https://scigraph.springernature.com/ontologies/product-market-codes/I2203X |
700 | 1 |
_aChang, Dong Eui. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783319753034 |
776 | 0 | 8 |
_iPrinted edition: _z9783319753058 |
830 | 0 |
_aSpringerBriefs in Computer Science, _x2191-5768 |
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_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=https://doi.org/10.1007/978-3-319-75304-1 |
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