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Computer vision with maker tech : detecting people with a Raspberry Pi, a thermal camera, and machine learning /
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. Youll learn the differ...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[Berkeley, Calif.] :
Apress,
[2021]
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Manganiello, Fabio, |e author. | |
| 245 | 1 | 0 | |a Computer vision with maker tech : |b detecting people with a Raspberry Pi, a thermal camera, and machine learning / |c Fabio Manganiello. |
| 264 | 1 | |a [Berkeley, Calif.] : |b Apress, |c [2021] | |
| 300 | |a 1 online resource : |b illustrations (chiefly color), charts (chiefly color) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a 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. Youll learn the differences between supervised and unsupervised learning and how the nuts-and-bolts of a neural network actually work. Youll 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, youll 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, youll put things together and work through a couple of practical examples. Youll 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 youll add a voice assistant that uses your own model to recognize your voice. You will: 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 | ||
| 505 | 0 | |a Chapter 1: Introduction to Machine Learning -- Chapter 2: Neural Networks -- Chapter 3: Computer Vision on Raspberry Pi. | |
| 588 | 0 | |a Online resource; title from PDF title page (SpringerLink, viewed March 22, 2021). | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Computer vision. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Internet of things. | |
| 650 | 0 | |a Electronic security systems |x Design and construction. | |
| 650 | 6 | |a Vision par ordinateur. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Internet des objets. | |
| 650 | 7 | |a Computer vision |2 fast | |
| 650 | 7 | |a Electronic security systems |x Design and construction |2 fast | |
| 650 | 7 | |a Internet of things |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
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| 994 | |a 92 |b IZTAP | ||