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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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Variance

Variance is the measure of how much the data varies from the mean. In the code that follows, we are using Koalas, a distributed clone of pandas, to do our basic data engineering tasks, such as determining variance. The following code uses standard deviation over a rolling window to show data spike issues:

import databricks.koalas as ks 

df = ks.DataFrame(pump_data)
print("variance: " + str(df.var()))
minuite['time'] = pd.to_datetime(minuite['time'])
minuite.set_index('time')
minuite['sample'] = minuite['sample'].rolling(window=600,center=False).std()
Duty cycles are used on IoT product lines before enough data is collected for machine learning. They are often simple measures, such as whether the device is too hot or there are too many vibrations.

We can also look at high and low values such as maximum to show whether the sensor is throwing out appropriate readings. The following code shows the maximum reading of our dataset:

max = DF.agg({"averageRating": "max"}).collect()[0]

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