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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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How it works...

YOLO looks at the image once and divides the image up into a grid. It then uses bounding boxes to divide up the grid. YOLO first determines whether the bounding box has an object and then determines the class of object. By incorporating a prefilter on the algorithm, this screens out parts of the images that are not objects and YOLO is then able to dramatically speed up its search.

In this example, after importing our libraries, we set our variables. First, we open yolov3.txt. This file contains the classes of the pretrained library we will be using. Next, we create a random color array to denote our different objects as different colors. Then we import our libraries and set our camera to the first camera on the computer. We then set thresholds and scale images so that the image sizes are something that would be recognizable to the classifier. If we, for example, add a high-resolution image, the classifier might only recognize very small things as objects while...

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