{"product_id":"learning-algorithms-for-internet-of-things-applying-python-tools-to-improve-data-collection-use-for-system-performance-paperback","title":"Learning Algorithms for Internet of Things: Applying Python Tools to Improve Data Collection Use for System Performance - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eG. R. Kanagachidambaresan\u003c\/b\u003e (Author), \u003cb\u003eN. Bharathi\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThe 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.\u003c\/p\u003e \u003cp\u003eMany 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.\u003c\/p\u003e \u003cp\u003e\u003cem\u003eLearning Algorithms for Internet of Things \u003c\/em\u003eprovides you with an easier way to understand the purpose and application of learning algorithms on IoT.\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eWhat you'll Learn\u003c\/strong\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eSupervised algorithms such as Regression and Classification.\u003c\/li\u003e \u003cli\u003eUnsupervised algorithms, like K-means clustering, KNN, hierarchical clustering, principal component analysis, and more.\u003c\/li\u003e \u003cli\u003eArtificial neural networks for IoT (architecture, feedback, feed-forward, unsupervised).\u003c\/li\u003e \u003cli\u003eConvolutional neural networks for IoT (general, LeNet, AlexNet, VGGNet, GoogLeNet, etc.).\u003c\/li\u003e \u003cli\u003eOptimization methods, such as gradient descent, stochastic gradient descent, Adagrad, AdaDelta, and IoT optimization.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003cstrong\u003eWho This Book Is For \u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eStudents interested in learning algorithms and their implementations, as well as researchers in IoT looking to extend their work with learning algorithms\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThe 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.\u003c\/p\u003e \u003cp\u003eMany 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.\u003c\/p\u003e \u003cp\u003e\u003cem\u003eLearning Algorithms for Internet of Things \u003c\/em\u003eprovides you with an easier way to understand the purpose and application of learning algorithms on IoT.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eDr. G. R. Kanagachidambaresan\u003c\/strong\u003e is a Professor in the Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R\u0026amp;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.\u003c\/p\u003e \u003cp\u003eHe 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.\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eDr. N. Bharathi\u003c\/strong\u003e 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\u0026amp;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.\u003c\/p\u003e \u003cp\u003eShe 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\u0026amp;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.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 299\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.67 x 9.21 x 6.14 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e December 20, 2024\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":53387493572915,"sku":"9798868805295","price":55.58,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0300\/5595\/6612\/files\/7iPqxdIcUn9798868805295.webp?v=1779440192","url":"https:\/\/www.vysn.com\/en-ca\/products\/learning-algorithms-for-internet-of-things-applying-python-tools-to-improve-data-collection-use-for-system-performance-paperback","provider":"VYSN","version":"1.0","type":"link"}