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.
The audience for this book is anyone who has a basic knowledge of developing IoT applications and Python and wants to make their IoT applications smarter by applying AI techniques. This audience may include the following people:
- IoT practitioners who already know how to build IoT systems, but now they want to implement AI to make their IoT solution smart.
- Data science practitioners who have been building analytics with IoT platforms, but now they want to transition from IoT analytics to IoT AI, thus making their IoT solutions smarter.
- Software engineers who want to develop AI-based solutions for smart IoT devices.
- Embedded system engineers looking to bring smartness and intelligence to their products.
Chapter 1, Principles and Foundations of IoT and AI, introduces the basic concepts IoT, AI, and data science. We end the chapter with an introduction to the tools and datasets we will be using in the book.
Chapter 2, Data Access and Distributed Processing for IoT, covers various methods of accessing data from various data sources, such as files, databases, distributed data stores, and streaming data.
Chapter 3, Machine Learning for IoT, covers the various aspects of machine learning, such as supervised, unsupervised, and reinforcement learning for IoT. The chapter ends with tips and tricks to improve your models' performance.
Chapter 4, Deep Learning for IoT, explores the various aspects of deep learning, such as MLP, CNN, RNN, and autoencoders for IoT. It also introduces various frameworks for deep learning.
Chapter 5, Genetic Algorithms for IoT, discusses optimization and different evolutionary techniques employed for optimization with an emphasis on genetic algorithms.
Chapter 6, Reinforcement Learning for IoT, introduces the concepts of reinforcement learning, such as policy gradients and Q-networks. We cover how to implement deep Q networks using TensorFlow and learn some cool real-world problems where reinforcement learning can be applied.
Chapter 7, Generative Models for IoT, introduces the concepts of adversarial and generative learning. We cover how to implement GAN, DCGAN, and CycleGAN using TensorFlow, and also look at their real-life applications.
Chapter 8, Distributed AI for IoT, covers how to leverage machine learning in distributed mode for IoT applications.
Chapter 9, Personal and Home and IoT, goes over some exciting personal and home applications of IoT.
Chapter 10, AI for Industrial IoT, explains how to apply the concepts learned in this book to two case studies with industrial IoT data.
Chapter 11, AI for Smart Cities IoT, explains how to apply the concepts learned in this book to IoT data generated from smart cities.
Chapter 12, Combining It All Together, covers how to pre-process textual, image, video, and audio data before feeding it to models. It also introduces time series data.
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