Computer vision (CV) is a fascinating field of study that attempts to automate the process of assigning meaning to digital images or videos. In other words, we are helping computers see and understand the world around us! A number of machine learning (ML) algorithms and techniques can be used to accomplish CV tasks, and as ML becomes faster and more efficient, we can deploy these techniques to embedded systems.
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Computer Vision with Embedded Machine Learning
Edge ImpulseÃœber diesen Kurs
29.428 kürzliche Aufrufe
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100Â % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Stufe „Mittel“
Some math (reading plots, arithmetic, and algebra) is required in the course. Experience with the Python is recommended to complete the projects.
Ca. 31 Stunden zum Abschließen
Englisch
Untertitel: Englisch
Was Sie lernen werden
How to train and develop an image classification system using machine learning
How to train and develop an object detection system using machine learning
How to deploy a machine learning model to a microcontroller
Kompetenzen, die Sie erwerben
- Machine Learning
- Python Programming
- Microcontroller
- Embedded System Development
- Computer Programming
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100Â % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Stufe „Mittel“
Some math (reading plots, arithmetic, and algebra) is required in the course. Experience with the Python is recommended to complete the projects.
Ca. 31 Stunden zum Abschließen
Englisch
Untertitel: Englisch
von
Edge Impulse
Edge Impulse is the leading development platform for machine learning on edge devices, free for developers and trusted by enterprises. Founded in 2019 by Zach Shelby and Jan Jongboom, we are on a mission to enable developers to create the next generation of intelligent devices. We believe that machine learning can enable positive change in society, and we are dedicated to support applications for good.
Lehrplan - Was Sie in diesem Kurs lernen werden
11 Stunden zum Abschließen
Image Classification
In this module, we introduce the concept of computer vision and how it can be used to solve problems. We cover how digital images are created and stored on a computer. Next, we review neural networks and demonstrate how they can be used to classify simple images. Finally, we walk you through a project to train an image classifier and deploy it to an embedded system.
11 Stunden zum Abschließen
13 Videos (Gesamt 185 min), 14 Lektüren, 4 Quiz
11 Stunden zum Abschließen
Convolutional Neural Networks
In this module, we go over the basics of convolutional neural networks (CNNs) and how they can be used to create a more robust image classification model. We look at the internal workings of CNNs (e.g. convolution and pooling) along with some visualization techniques used to see how CNNs make decisions. We introduce the concept of data augmentation to help provide more data to the training process. You will have the opportunity to train your own CNN and deploy it to an embedded system.
11 Stunden zum Abschließen
9 Videos (Gesamt 114 min), 13 Lektüren, 5 Quiz
9 Stunden zum Abschließen
Object Detection
In this module, we will cover the basics of object detection and how it differs from image classification. We will go over the math involved to measure objection detection performance. After, we will introduce several popular object detection models and demonstrate the process required to train such a model in Edge Impulse. Finally, you will be asked to deploy an object detection model to an embedded system.
9 Stunden zum Abschließen
10 Videos (Gesamt 109 min), 10 Lektüren, 3 Quiz
Bewertungen
- 5 stars73,58Â %
- 4 stars24,52Â %
- 3 stars1,88Â %
Top-Bewertungen von COMPUTER VISION WITH EMBEDDED MACHINE LEARNING
von ML23. Sep. 2021
Great course, Shawn always explains things in a clear and engaging way, with a strong focus on the application of the concepts. I'm definitely looking forward to more courses on embedded ML!
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