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Hands-On Artificial Intelligence for IoT: Expert machine learning and deep learning techniques for developing smarter IoT systems
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About Hands-On Artificial Intelligence For IoT: Expert
Product Description Build smarter systems by combining artificial intelligence and the Internet of Things―two of the most talked about topics today Key Features Leverage the power of Python libraries such as TensorFlow and Keras to work with real-time IoT data Process IoT data and predict outcomes in real time to build smart IoT models Cover practical case studies on industrial IoT, smart cities, and home automation Book Description There are many applications that use data science and analytics to gain insights from terabytes of data. These apps, however, do not address the challenge of continually discovering patterns for IoT data. In Hands-On Artificial Intelligence for IoT, we cover various aspects of artificial intelligence (AI) and its implementation to make your IoT solutions smarter. This book starts by covering the process of gathering and preprocessing IoT data gathered from distributed sources. You will learn different AI techniques such as machine learning, deep learning, reinforcement learning, and natural language processing to build smart IoT systems. You will also leverage the power of AI to handle real-time data coming from wearable devices. As you progress through the book, techniques for building models that work with different kinds of data generated and consumed by IoT devices such as time series, images, and audio will be covered. Useful case studies on four major application areas of IoT solutions are a key focal point of this book. In the concluding chapters, you will leverage the power of widely used Python libraries, TensorFlow and Keras, to build different kinds of smart AI models. By the end of this book, you will be able to build smart AI-powered IoT apps with confidence. What you will learn Apply different AI techniques including machine learning and deep learning using TensorFlow and Keras Access and process data from various distributed sources Perform supervised and unsupervised machine learning for IoT data Implement distributed processing of IoT data over Apache Spark using the MLLib and H2O.ai platforms Forecast time-series data using deep learning methods Implementing AI from case studies in Personal IoT, Industrial IoT, and Smart Cities Gain unique insights from data obtained from wearable devices and smart devices Who this book is for If you are a data science professional or a machine learning developer looking to build smart systems for IoT, Hands-On Artificial Intelligence for IoT is for you. If you want to learn how popular artificial intelligence (AI) techniques can be used in the Internet of Things domain, this book will also be of benefit. A basic understanding of machine learning concepts will be required to get the best out of this book. Table of Contents Principles and Foundations of IoT and AI Data Access and Distributed Processing for IoT Machine Learning for IoT Deep Learning for IoT Genetic Algorithms for IoT Reinforcement Learning for IoT GAN for IoT Distributed AI for IoT Personal and Home and IoT AI for Industrial IoT AI for Smart Cities IoT Combining It All Together About the Author Amita Kapoor, is Associate Professor in the Department of Electronics, SRCASW, University of Delhi. She has been actively teaching neural networks for the last twenty years. She did her Masters in Electronics in 1996, and her PhD in 2011. During the course of her PhD she was awarded prestigious DAAD fellowship to pursue a part of her research work in Karlsruhe Institute of Technology, Karlsruhe, Germany. She had been awarded best Presentation Award at the Photonics 2008 international conference for her paper. She is a member of professional bodies such as OSA (Optical Society of America), IEEE (Institute of Electrical and Electronics Engineers), INNS (International Neural Network Society), and ISBS (Indian society for Buddhist Studies). She has more than 40 publications in international journals and conferences. Her present research areas include Machine Learning, Arti
