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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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Computer Vision

Computer vision has come a long way in recent years. Unlike many other forms of machine learning that require complex analysis, the vast majority of computer vision problems come from simple RGB cameras. Machine learning frameworks such as Keras and OpenCV have standard and high-accuracy neural networks built-in. A few years ago, implementing a facial recognition neural net, for example, was complex and challenging to set up in Python, let alone on a high-speed device using C++ or CUDA. Today, this process is easier and more accessible than ever before. In this chapter, we are going to talk about implementing computer vision in the cloud, as well as on Edge devices such as NVIDIA Jetson Nano.

We will cover the following recipes in this chapter: 

  • Connecting cameras through OpenCV
  • Using Microsoft's custom vision to train and label your images
  • Detecting...

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