First, import the libraries and set the settings. In the next step, we import the opencv and python libraries and we also import time so we can wait if the camera is not ready. Next, we set some debugging flags so we can test the output visually if we are debugging. Then we import the Haar Cascade XML file into our classifier. Finally, we open the first video camera attached to the machine. In step 2, we wait for the camera to become ready. This is often not a problem when developing the software as the system has already recognized the camera. Then we set this program to run automatically; the camera may not be available for up to a minute when the system is restarted. We are also starting an infinite loop of processing the camera images. In the next step, we capture and transform the image into black and white. Next, we run the classifier. The detectMultiScale classifier allows faces of different sizes to be detected. The minNeighbors parameter specifies...
Artificial Intelligence for IoT Cookbook
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Artificial Intelligence for IoT Cookbook
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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.
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Current Title:
Artificial Intelligence for IoT Cookbook
Table of Contents (11 chapters)
Preface
Setting Up the IoT and AI Environment
Free Chapter
Handling Data
Machine Learning for IoT
Deep Learning for Predictive Maintenance
Anomaly Detection
Computer Vision
NLP and Bots for Self-Ordering Kiosks
Optimizing with Microcontrollers and Pipelines
Deploying to the Edge
Customer Reviews