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Artificial Intelligence for IoT Cookbook

Artificial Intelligence for IoT Cookbook

By : Roshak
4.9 (10)
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Artificial Intelligence for IoT Cookbook

Artificial Intelligence for IoT Cookbook

4.9 (10)
By: Roshak

Overview of this book

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.
Table of Contents (11 chapters)
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Face detection on constrained devices

Deep neural networks tend to outperform other classification techniques. However, with IoT devices, there is not a large amount of RAM, compute, or storage. On constrained devices, RAM and storage are often in MB and not in GB, making traditional classifiers not possible. Some video classification services in the cloud charge over $10,000 per device for live streaming video. OpenCV's Haar classifiers have the same underlying principles as a convolutional neural network but at a fraction of the compute and storage. OpenCV is available in multiple languages and runs on some of the most constrained devices.

In this recipe, we are going to set up a Haar Cascade to detect if a person is close to the camera. This is often used in Kiosk and other interactive smart devices. The Haar Cascade can be run at a high rate of speed and when it finds a face that is close to the machine it can send that image via a cloud service or a different onboard machine...

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