In the Internet of Things (IoT) era, a huge large number of sensing devices collect and generate various sensory data over time for a wide range of applications. This data is predominantly made up of big, fast, and real-time streams based on the applications. The use of analytics in relation to such big data or data streams is crucial for learning new information, predicting future insights, and making informed decisions, which makes IoT a worthy paradigm for businesses and a quality-of-life improving technology.
This book will provide you with a thorough overview of a class of advanced machine learning techniques called deep learning (DL), to facilitate the analytics and learning in various IoT applications. A hands-on overview will take you through what each process is, from data collection, analysis, modeling, and a model's performance evaluation, to various IoT application and deployment settings.
You’ll learn how to train convolutional neural networks (CNN) for developing applications for image-based road faults detection and smart garbage separation, followed by implementing voice-initiated smart light control and home access mechanisms powered by recurrent neural networks (RNN).
You’ll master IoT applications for indoor localization, predictive maintenance, and locating equipment in a large hospital using autoencoders, DeepFi, and LSTM networks. Furthermore, you'll learn IoT application development for healthcare with IoT security enhanced. After reading this book, you will have a good head start at developing more complex DL applications for IoT-enabled devices.
Last but not least, this book isn't meant to be read cover to cover. You can turn the pages to a chapter that looks like something you're trying to accomplish or that ignites your interest. If you notice any errors or glaring omissions, better an errata than never or let us know or file issues on GitHub repo of this book. Thank you! Happy reading!