Hands-On Artificial Intelligence for IoT: Expert machine learning and deep learning techniques for developing smarter IoT systems (English Edition)

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

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