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Learning algorithms for Internet of ...

Kanagachidambaresan, G. R.

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Learning algorithms for Internet of Things = applying Python tools to improve data collection use for system performance /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learning algorithms for Internet of Things/ by G.R. Kanagachidambaresan, N. Bharathi.
Reminder of title:	applying Python tools to improve data collection use for system performance /
Author:	Kanagachidambaresan, G. R.
other author:	Bharathi, N.
Published:	Berkeley, CA :Apress : : 2024.,
Description:	xix, 299 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 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.
Contained By:	Springer Nature eBook
Subject:	Internet of things. -
Online resource:	https://doi.org/10.1007/979-8-8688-0530-1
ISBN:	9798868805301

Learning algorithms for Internet of Things = applying Python tools to improve data collection use for system performance /
Kanagachidambaresan, G. R.

Learning algorithms for Internet of Thingsapplying Python tools to improve data collection use for system performance /[electronic resource] :by G.R. Kanagachidambaresan, N. Bharathi. - Berkeley, CA :Apress :2024. - xix, 299 p. :ill., digital ;24 cm. - Maker innovations series,2948-2550. - Maker innovations series..

Chapter 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.

The 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.

ISBN: 9798868805301

Standard No.: 10.1007/979-8-8688-0530-1doiSubjects--Topical Terms:

2057703
Internet of things.

LC Class. No.: TK5105.8857

Dewey Class. No.: 004.678

Learning algorithms for Internet of Things = applying Python tools to improve data collection use for system performance /
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505 0 $a Chapter 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 $a The 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.
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