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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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Training vision with PyTorch on GPUs

In the previous recipe, we implemented an object classifier using GPU and an NVIDIA Jetson Nano. There are other types of GPU-enabled devices. These range from the NVIDIA TX2, which can be put on a drone to do real-time analysis of pipelines, to industrial PCs running GPUs and using computer vision to perform analyses on workplace safety. In this recipe, we are going to train and add to an existing image classification model by adding our own images to it.

Challenges that the IoT faces include over-the-air (OTA) updates and fleet management. IoT Edge is a conceptual framework that solves this. In OTA updates, Docker containers are used as an update mechanism. The underlying systems can be updated without having to worry about complete device failure. If an update does not work, the system can be rolled back because container failures do not affect the main OS and the Docker daemon can perform the update and roll back.

In this recipe, we are going...

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