Learning Algorithms for Internet of Things: Applying Python Tools to Improve Data Collection Use for System Performance

Kanagachidambaresan, G. R., Bharathi, N.

Learning Algorithms for Internet of Things: Applying Python Tools to Improve Data Collection Use for System Performance

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商品描述

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.

What you'll Learn

  • Supervised algorithms such as Regression and Classification.
  • Unsupervised algorithms, like K-means clustering, KNN, hierarchical clustering, principal component analysis, and more.
  • Artificial neural networks for IoT (architecture, feedback, feed-forward, unsupervised).
  • Convolutional neural networks for IoT (general, LeNet, AlexNet, VGGNet, GoogLeNet, etc.).
  • Optimization methods, such as gradient descent, stochastic gradient descent, Adagrad, AdaDelta, and IoT optimization.

Who This Book Is For

Students interested in learning algorithms and their implementations, as well as researchers in IoT looking to extend their work with learning algorithms

商品描述(中文翻譯)

物聯網（IoT）的出現為環境感知和智能反應鋪平了道路。透過機器學習和深度學習技術的支持，這一點可以進一步改善。本書描述了可以應用於基於IoT的實時應用的學習算法，並提高所收集數據的利用率及系統的整體性能。

許多社會挑戰和問題可以通過更好地結合IoT和學習算法來解決。「智能」這一術語只有在學習算法的幫助下才能實現。此外，本書還為研究人員提供了專注於學習算法在IoT應用（如醫療保健、農業、交通等）中的實施和性能的代碼片段。這些片段包括Python套件，如Scipy、Scikit-learn、Theano、TensorFlow、Keras、PyTorch等。

《物聯網的學習算法》為您提供了一種更簡單的方式來理解學習算法在IoT上的目的和應用。

您將學到的內容：
- 監督式算法，如回歸和分類。
- 非監督式算法，如K-means聚類、KNN、層次聚類、主成分分析等。
- 用於IoT的人工神經網絡（架構、反饋、前饋、非監督）。
- 用於IoT的卷積神經網絡（一般、LeNet、AlexNet、VGGNet、GoogLeNet等）。
- 優化方法，如梯度下降、隨機梯度下降、Adagrad、AdaDelta和IoT優化。

本書適合對學習算法及其實施感興趣的學生，以及希望將其工作擴展到學習算法的IoT研究人員。

作者簡介

Dr. G. R. Kanagachidambaresan is a Professor in the Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology. His main research interests includes IoT, Expert systems and Sensors. He has published several reputed articles and undertaken several consultancy activities for leading MNC companies. He has also guest edited several special issue volumes and books at Springer and serves on the editorial review board for peer reviewed journals.

He is currently working on several government-sponsored research projects like ISRO, DBT and DST. He is a TEC committee member in DBT. He is an ASEM-DUO Fellow and has successfully edited several books in EAI Springer. He is currently the Editor-in-Chief for the Next Generation Computer and Communication Engineering Series (Wiley). He received his B.E degree in Electrical and Electronics Engineering from Anna University in 2010 and M.E. Pervasive Computing Technologies in Anna University in 2012. He has completed his Ph.D. in Anna University Chennai in 2017.

Dr. N. Bharathi is an Associate Professor in the Computer Science Engineering Department at SRM Institute of Science and Technology in Chennai, India. Her past experiences are Associate Professor at Saveetha School of Engineering, R&D head at Yalamanchili Manufacturing Private Limited, and Assistant professor in SASTRA deemed university. She has good knowledge to work with IoT and embedded system in addition to computer science engineering concepts.

She was awarded with a Ph.D. degree in Computer Science in 2014 from SASTRA Deemed University, with 19+ years of work experience as an academic and industrial experience as R&D head involved in ARM platform boards with software development in Ubuntu OS. She completed her M. Tech in Advanced computing in SASTRA deemed University and done her M.Tech project internship at Center for High Performance Embedded System (CHiPES), Nanyang Technological University (NTU), Singapore. She was completed her B.E. in computer science engineering in 2002 at Shanmugha College of Engineering (Bharathidasan University). She published many research papers in reputed journals and conferences along with book chapters, advised many B.Tech. and M.Tech. students in various domains of computer science engineering and embedded systems and is currently advising four research scholars.

作者簡介(中文翻譯)

Dr. G. R. Kanagachidambaresan 是 Vel Tech Rangarajan Dr. Sagunthala 研究與科技學院計算機科學與工程系的教授。他的主要研究興趣包括物聯網（IoT）、專家系統和感測器。他已發表多篇知名文章，並為多家領先的跨國公司進行了多項顧問活動。他還擔任過 Springer 的多個特刊和書籍的客座編輯，並在同行評審期刊的編輯審查委員會中任職。

他目前正在進行多個政府資助的研究項目，如 ISRO、DBT 和 DST。他是 DBT 的 TEC 委員會成員。他是 ASEM-DUO 獎學金得主，並成功編輯了多本 EAI Springer 的書籍。目前，他是 Next Generation Computer and Communication Engineering Series（Wiley）的主編。他於 2010 年在安娜大學獲得電氣與電子工程學士學位，並於 2012 年在安娜大學獲得普遍計算技術碩士學位。他於 2017 年在安娜大學金奈完成博士學位。

Dr. N. Bharathi 是印度金奈 SRM 科學與技術學院計算機科學工程系的副教授。她的過去經歷包括在 Saveetha 工程學院擔任副教授、在 Yalamanchili Manufacturing Private Limited 擔任研發主管，以及在 SASTRA 認可大學擔任助理教授。除了計算機科學工程概念外，她對物聯網和嵌入式系統也有良好的工作知識。

她於 2014 年在 SASTRA 認可大學獲得計算機科學博士學位，擁有超過 19 年的學術和工業經驗，曾擔任 ARM 平台板的研發主管，並參與 Ubuntu OS 的軟體開發。她在 SASTRA 認可大學完成了高級計算的碩士學位，並在新加坡南洋理工大學（NTU）的高效能嵌入式系統中心（CHiPES）完成了碩士專案實習。她於 2002 年在 Shanmugha 工程學院（Bharathidasan 大學）獲得計算機科學工程學士學位。她在知名期刊和會議上發表了多篇研究論文及書籍章節，指導了多名 B.Tech. 和 M.Tech. 學生於計算機科學工程和嵌入式系統的各個領域，目前正在指導四名研究學者。

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