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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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This recipe is dependent on other components. These components are in the GitHub repository for this chapter, under AI_Benchtest. You can start the programs by going into their respective folders and running docker or docker-compose. To run the camera server in a Terminal, go into the AI_Benchtest_API folder and run the following command:

docker-compose up

Next, you must run the AI_Benchtest_Cam module. In a Terminal, CD into the AI_Benchtest_Cam folder and run the same docker-compose command that you ran to get the API server running. At this point, both the camera and compute servers will be up and running and transmitting their status to the API server. Next, you will need to run a UI server so that you can give commands to the other servers. To do this, CD into the AI_Benchtest_API folder and run the following docker command to start the UI application:

docker build -t sample:dev . docker run -v ${PWD}:/app -v /app/node_modules -p 3001:3000 --rm...

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