Book Image

Artificial Intelligence for IoT Cookbook

By : Michael Roshak
Book Image

Artificial Intelligence for IoT Cookbook

By: Michael 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)

Getting ready

From a coding perspective, using OpenCV abstracts the hardware away. It does not matter if you are using a $5 Raspberry Pi Zero or a $120 LattePanda; the only things required for this recipe are a computer and a camera. Most laptops have built-in cameras, but for a desktop computer or a single board computer (SBC), such as a Raspberry Pi or LattePanda, you will need a USB web camera.

Next, you will need to install OpenCV. As mentioned earlier, there are ways of getting OpenCV on constrained devices. These are all unique to the device in question. In our case, we will put a PiCam module on a Raspberry Pi Zero. The following is an image of a PiCam module for reference:

To add the PiCam to the Pi Zero, you simply pull the black tabs from the connector, insert the PiCam module, and then push in the tab, as shown in the following image:

From here you need to enable the cameras in your Raspberry Pi. You will need to plug a monitor, keyboard, and mouse...