Computer Vision with Maker Tech
Detecting People With a Raspberry Pi, a Thermal Camera, and Machine Learning
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Computer Vision with Maker Tech
Description
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
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.
Shipping & Processing
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How accurate is availability?
Availability is based on the stock levels our suppliers tell us they hold, and we update it regularly. If a supplier issue affects your order, we’ll let you know with an updated estimate.
Delivery
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- Australia Post usually delivers within 2–10 business days of shipping (around 4 in metro areas and around 6 in regional areas).
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- Supplier delays: Occasionally a supplier is delayed, or their stock information turns out to be inaccurate. We’ll keep your order open for up to 60 days. If an item hasn’t shipped or isn’t on its way to us by then, we’ll cancel that item and refund it.
Faulty or damaged items are always covered. See Returns.
Returns
Damaged or incorrect items (within 30 days)
If an item arrives damaged or isn’t what you ordered, contact us within 30 days of delivery. We may ask for a photo, and if we need the item back we’ll send a prepaid return label, so it costs you nothing. You’ll receive a replacement or refund, as guaranteed under the Australian Consumer Law.
Change of mind (within 30 days)
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- the item is unused and in its original condition and packaging;
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Returned to sender
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