The mission of this book is to enable the reader to build AI-enabled IoT applications. With the surge in popularity of IoT devices, there are many applications that use data science and analytics to utilize the terabyte of data generated. However, these applications do not address the challenge of continually discovering patterns in IoT data. In this book, we cover the various aspects of AI theory and implementation that the reader can utilize to make their IoT solutions smarter by implementing AI techniques.
The reader starts by learning the basics of AI and IoT devices and how to read IoT data from various sources and streams. Then we introduce various ways to implement AI with examples in TensorFlow, scikit learn, and Keras. The topics covered include machine learning, deep learning, genetic algorithms, reinforcement learning, and generative adversarial networks. We also show the reader how to implement AI using distributed technologies and on the cloud. Once the reader is familiar with AI techniques, then we introduce various techniques for different kinds of data generated and consumed by IoT devices, such as time series, images, audio, video, text, and speech.
After explaining various AI techniques on various kinds of IoT data, finally, we share some case studies with the reader from the four major categories of IoT solutions: personal IoT, home IoT, industrial IoT, and smart city IoT.
