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_a9798868805301 _9979-8-8688-0530-1 |
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_a10.1007/979-8-8688-0530-1 _2doi |
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_aUT _2bicssc |
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_aCOM043000 _2bisacsh |
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_aUT _2thema |
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_aKanagachidambaresan, G.R. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _989295 |
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| 245 | 1 | 0 |
_aLearning Algorithms for Internet of Things _h[electronic resource] : _bApplying Python Tools to Improve Data Collection Use for System Performance / _cby G.R. Kanagachidambaresan, N. Bharathi. |
| 250 | _a1st ed. 2024. | ||
| 264 | 1 |
_aBerkeley, CA : _bApress : _bImprint: Apress, _c2024. |
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| 300 |
_aXIX, 299 p. 64 illus. _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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| 490 | 1 |
_aMaker Innovations Series, _x2948-2550 |
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| 505 | 0 | _aChapter 1: Learning Algorithms for IoT -- Chapter 2: Python Packages for Learning Algorithms -- Chapter 3: Supervised Algorithms -- Chapter 4: Unsupervised Algorithms -- Chapter 5: Reinforcement Algorithms -- Chapter 6: Artificial Neural Networks for IoT -- Chapter 7: Convolutional Neural Networks for IoT -- Chapter 8: LSTM, GAN, and RNN -- Chapter 9: Optimization Methods. | |
| 520 | _aThe advent of Internet of Things (IoT) has paved the way for sensing the environment and smartly responding. This can be further improved by enabling intelligence to the system with the support of machine learning and deep learning techniques. This book describes learning algorithms that can be applied to IoT-based, real-time applications and improve the utilization of data collected and the overall performance of the system. Many societal challenges and problems can be resolved using a better amalgamation of IoT and learning algorithms. “Smartness” is the buzzword that is realized only with the help of learning algorithms. In addition, it supports researchers with code snippets that focus on the implementation and performance of learning algorithms on IoT based applications such as healthcare, agriculture, transportation, etc. These snippets include Python packages such as Scipy, Scikit-learn, Theano, TensorFlow, Keras, PyTorch, and more. Learning Algorithms for Internet of Things provides you with an easier way to understand the purpose and application of learning algorithms on IoT. | ||
| 650 | 0 |
_aInternet of things. _958845 |
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| 650 | 0 | _aTelecommunication. | |
| 650 | 0 |
_aPython (Computer program language). _964608 |
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| 650 | 1 | 4 |
_aInternet of Things. _958845 |
| 650 | 2 | 4 | _aCommunications Engineering, Networks. |
| 650 | 2 | 4 |
_aPython. _947553 |
| 700 | 1 |
_aBharathi, N. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _989296 |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9798868805295 |
| 776 | 0 | 8 |
_iPrinted edition: _z9798868805318 |
| 776 | 0 | 8 |
_iPrinted edition: _z9798868813641 |
| 830 | 0 |
_aMaker Innovations Series, _x2948-2550 _968066 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/979-8-8688-0530-1 |
| 912 | _aZDB-2-CWD | ||
| 912 | _aZDB-2-SXPC | ||
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_c65906 _d65906 |
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