Computer Vision with Maker Tech: Detecting People With a Raspberry Pi, a Thermal Camera, and Machine Learning

✍ Scribed by Fabio Manganiello

• Publisher: Apress
• Year: 2021
• Tongue: English
• Leaves: 243
• Category: Library

✦ Synopsis

https://www.apress.com/gp/book/9781484268209

Harness the untapped potential of combining a decentralized Internet of Things (IoT) with the ability to make predictions on real-world fuzzy data. This book covers the theory behind machine learning models and shows you how to program and assemble a voice-controlled security.

You’ll learn the differences between supervised and unsupervised learning and how the nuts-and-bolts of a neural network actually work. You’ll also learn to identify and measure the metrics that tell how well your classifier is doing. An overview of other types of machine learning techniques, such as genetic algorithms, reinforcement learning, support vector machines, and anomaly detectors will get you up and running with a familiarity of basic machine learning concepts. Chapters focus on the best practices to build models that can actually scale and are flexible enough to be embedded in multiple applications and easily reusable.

With those concepts covered, you’ll dive into the tools for setting up a network to collect and process the data points to be fed to our models by using some of the ubiquitous and cheap pieces of hardware that make up today's home automation and IoT industry, such as the RaspberryPi, Arduino, ESP8266, etc. Finally, you’ll put things together and work through a couple of practical examples. You’ll deploy models for detecting the presence of people in your house, and anomaly detectors that inform you if some sensors have measured something unusual. And you’ll add a voice assistant that uses your own model to recognize your voice. 

What You'll Learn

• Develop a voice assistant to control your IoT devices
• Implement Computer Vision to detect changes in an environment
• Go beyond simple projects to also gain a grounding machine learning in general
• See how IoT can become "smarter" with the inception of machine learning techniques
• Build machine learning models using TensorFlow and OpenCV

Who This Book Is For

Makers and amateur programmers interested in taking simple IoT projects to the next level using TensorFlow and machine learning. Also more advanced programmers wanting an easy on ramp to machine learning concepts.

✦ Table of Contents

Table of Contents
About the Author
About the Technical Reviewer
Introduction
Chapters at a Glance
Chapter 1: Introduction to Machine Learning
1.1 History
1.2 Supervised and unsupervised learning
1.3 Preparing your tools
1.3.1 Software tools
1.3.2 Setting up your environment
1.4 Linear regression
1.4.1 Loading and plotting the dataset
1.4.2 The idea behind regression
1.4.3 Gradient descent
1.4.4 Input normalization
1.4.5 Defining and training the model
1.4.6 Evaluating your model
1.4.7 Saving and loading your model
1.5 Multivariate linear regression
1.5.1 Redundant features
1.5.2 Principal component analysis
1.5.3 Training set and test set
1.5.4 Loading and visualizing the dataset
1.6 Polynomial regression
1.7 Normal equation
1.8 Logistic regression
1.8.1 Cost function
1.8.2 Building the regression model from scratch
1.8.3 The TensorFlow way
1.8.4 Multiclass regression
1.8.5 Non-linear boundaries
Chapter 2: Neural Networks
2.1 Back-propagation
2.2 Implementation guidelines
2.2.1 Underfit and overfit
2.3 Error metrics
2.4 Implementing a network to recognize clothing items
2.5 Convolutional neural networks
2.5.1 Convolutional layer
2.5.2 Pooling layer
2.5.3 Fully connected layer and dropout
2.5.4 A network for recognizing images of fruits
Chapter 3: Computer Vision on Raspberry Pi
3.1 Preparing the hardware and the software
3.1.1 Preparing the hardware
3.1.2 Preparing the operating system
3.2 Installing the software dependencies
3.3 Capturing the images
3.4 Labelling the images
3.5 Training the model
3.6 Deploying the model
3.6.1 The OpenCV way
3.6.2 The TensorFlow way
3.7 Building your automation flows
3.8 Building a small home surveillance system
3.9 Live training and semi-supervised learning
3.10 Classifiers as a service
Bibliography
Index