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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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Setting Up the IoT and AI Environment

The Internet of Things (IoT) and artificial intelligence (AI) are leading to a dramatic impact on people's lives. Industries such as medicine are being revolutionized by wearable sensors that can monitor patients after they leave the hospital. Machine learning (ML) used on industrial devices is leading to better monitoring and less downtime with techniques such as anomaly detection, predictive maintenance, and prescriptive actions.

Building an IoT device capable of delivering results relies on gathering the right information. This book gives recipes that support the end-to-end IoT/ML life cycle. The next chapter has recipes for making sure that devices have the right sensors and the data is the best it can be for ML outcomes. Tools such as explanatory factor analysis and data collection design are used.

This chapter will cover the...

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