We create two files – one that gathers information (called Gather.py) and another that detects the anomalies on the device (called AnomalyDetection.py). In the Gather.py file, we import the classes, initialize SenseHat, set a variable for the number of readings we will be collecting, get both the gyroscopic and accelerometer readings, create an array of normal anonymous strings, and set the initial gyroscope and sensor ratings. Then we loop through our actions and tell the user to press Enter when they want to record normal greetings, and then tell them to press Enter when they want to record anomalous readings. From there, we gather data and give feedback to the user to let them know how many more data points they will be gathering. At this point, you should be using the device in a way that is normal for its use, such as fall detection by holding it close to your body. Then, for the next loop of anomalous readings, you drop the device. Finally, we create...
Artificial Intelligence for IoT Cookbook
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Artificial Intelligence for IoT Cookbook
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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.
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Current Title:
Artificial Intelligence for IoT Cookbook
Table of Contents (11 chapters)
Preface
Setting Up the IoT and AI Environment
Free Chapter
Handling Data
Machine Learning for IoT
Deep Learning for Predictive Maintenance
Anomaly Detection
Computer Vision
NLP and Bots for Self-Ordering Kiosks
Optimizing with Microcontrollers and Pipelines
Deploying to the Edge
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