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学生对 Edge Impulse 提供的 Computer Vision with Embedded Machine Learning 的评价和反馈

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14 个评分
3 条评论

课程概述

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. This course, offered by a partnership among Edge Impulse, OpenMV, Seeed Studio, and the TinyML Foundation, will give you an understanding of how deep learning with neural networks can be used to classify images and detect objects in images and videos. You will have the opportunity to deploy these machine learning models to embedded systems, which is known as embedded machine learning or TinyML. Familiarity with the Python programming language and basic ML concepts (such as neural networks, training, inference, and evaluation) is advised to understand some topics as well as complete the projects. Some math (reading plots, arithmetic, algebra) is also required for quizzes and projects. If you have not done so already, taking the "Introduction to Embedded Machine Learning" course is recommended. This course covers the concepts and vocabulary necessary to understand how convolutional neural networks (CNNs) operate, and it covers how to use them to classify images and detect objects. The hands-on projects will give you the opportunity to train your own CNNs and deploy them to a microcontroller and/or single board computer....

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1 - Computer Vision with Embedded Machine Learning 的 3 个评论(共 3 个)

创建者 Michal L

Sep 24, 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!

创建者 Julian L

Sep 24, 2021

It´s a great Course . Thanks

创建者 Ralph Y

Oct 24, 2021

Helped understand new concepts