Hands-On Artificial Intelligence for IoT

▶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

▶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

▶Who This Book Is For
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.

▶What this book covers
⦁ 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.