000 | 03235nam a22005535i 4500 | ||
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001 | 978-981-13-5850-0 | ||
003 | DE-He213 | ||
005 | 20240423130132.0 | ||
007 | cr nn 008mamaa | ||
008 | 190413s2019 si | s |||| 0|eng d | ||
020 |
_a9789811358500 _9978-981-13-5850-0 |
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024 | 7 |
_a10.1007/978-981-13-5850-0 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
072 | 7 |
_aUYQ _2bicssc |
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_aUYQ _2thema |
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_a006.3 _223 |
100 | 1 |
_aGhatak, Abhijit. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
245 | 1 | 0 |
_aDeep Learning with R _h[electronic resource] / _cby Abhijit Ghatak. |
250 | _a1st ed. 2019. | ||
264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2019. |
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300 |
_aXXIII, 245 p. 100 illus., 83 illus. in color. _bonline resource. |
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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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505 | 0 | _a Introduction to Machine Learning -- Introduction to Neural Networks -- Deep Neural Networks – I -- Initialization of Network Parameters -- Optimization -- Deep Neural Networks - II -- Convolutional Neural Networks (ConvNets) -- Recurrent Neural Networks (RNN) or Sequence Models -- Epilogue. | |
520 | _aDeep Learning with R introduces deep learning and neural networks using the R programming language. The book builds on the understanding of the theoretical and mathematical constructs and enables the reader to create applications on computer vision, natural language processing and transfer learning. The book starts with an introduction to machine learning and moves on to describe the basic architecture, different activation functions, forward propagation, cross-entropy loss and backward propagation of a simple neural network. It goes on to create different code segments to construct deep neural networks. It discusses in detail the initialization of network parameters, optimization techniques, and some of the common issues surrounding neural networks such as dealing with NaNs and the vanishing/exploding gradient problem. Advanced variants of multilayered perceptrons namely, convolutional neural networks and sequence models are explained, followed by application to different use cases. The book makes extensive use of the Keras and TensorFlow frameworks. . | ||
650 | 0 | _aArtificial intelligence. | |
650 | 0 |
_aComputer science _xMathematics. |
|
650 | 0 | _aComputer programming. | |
650 | 0 |
_aMathematical statistics _xData processing. |
|
650 | 1 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aMathematics of Computing. |
650 | 2 | 4 | _aProgramming Techniques. |
650 | 2 | 4 | _aStatistics and Computing. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9789811358494 |
776 | 0 | 8 |
_iPrinted edition: _z9789811358517 |
776 | 0 | 8 |
_iPrinted edition: _z9789811370892 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-13-5850-0 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c185421 _d185421 |