Artificial Intelligence for IoT Cookbook: Over 70 recipes for building AI solutions for smart homes, industrial IoT, and smart cities

English | 2021 | ISBN: 978-1838981983 | 260 Pages | PDF, EPUB, MOBI | 108 MB

Implement machine learning and deep learning techniques to perform predictive analytics on real-time IoT data
Artificial intelligence (AI) is rapidly finding practical applications across a wide variety of industry verticals, and the Internet of Things (IoT) is one of them. Developers are looking for ways to make IoT devices smarter and to make users’ lives easier. With this AI cookbook, you’ll be able to implement smart analytics using IoT data to gain insights, predict outcomes, and make informed decisions, along with covering advanced AI techniques that facilitate analytics and learning in various IoT applications.
Using a recipe-based approach, the book will take you through essential processes such as data collection, data analysis, modeling, statistics and monitoring, and deployment. You’ll use real-life datasets from smart homes, industrial IoT, and smart devices to train and evaluate simple to complex models and make predictions using trained models. Later chapters will take you through the key challenges faced while implementing machine learning, deep learning, and other AI techniques, such as natural language processing (NLP), computer vision, and embedded machine learning for building smart IoT systems. In addition to this, you’ll learn how to deploy models and improve their performance with ease.
By the end of this book, you’ll be able to package and deploy end-to-end AI apps and apply best practice solutions to common IoT problems.
What you will learn

• Explore various AI techniques to build smart IoT solutions from scratch
• Use machine learning and deep learning techniques to build smart voice recognition and facial detection systems
• Gain insights into IoT data using algorithms and implement them in projects
• Perform anomaly detection for time series data and other types of IoT data
• Implement embedded systems learning techniques for machine learning on small devices
• Apply pre-trained machine learning models to an edge device
• Deploy machine learning models to web apps and mobile using TensorFlow.js and Java