Applying Python Tools to Improve Data Collection Use for System Performance Kanagachidambaresan, G.R. creator author. aut http://id.loc.gov/vocabulary/relators/aut Bharathi, N. author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) text xxu 2024 1st ed. 2024. monographic eng

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access XIX, 299 p. 64 illus. online resource. 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. 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. by G.R. Kanagachidambaresan, N. Bharathi. Internet of things Telecommunication Python (Computer program language) Internet of Things Communications Engineering, Networks Python TK5105.8857 9798868805301 https://doi.org/10.1007/979-8-8688-0530-1 https://doi.org/10.1007/979-8-8688-0530-1 241220 20251030114631.0 979-8-8688-0530-1