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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/computer-vision-with-embedded-machine-learning
课程评论：没有评论
课程名称：嵌入式机器学习的计算机视觉 课程概述：计算机视觉（CV）是一个引人入胜的研究领域，旨在自动化地赋予数字图像或视频意义。简单来说，就是帮助计算机看懂周围的世界！为完成CV任务，许多机器学习（ML）算法和技术都可以被使用。随着ML技术的加速和高效，这些技术可以被部署到嵌入式系统中。本课程由Edge Impulse、OpenMV、Seeed Studio和TinyML Foundation联合提供，将帮助你理解如何利用深度学习和神经网络来分类图像和检测图像及视频中的物体。你将有机会将这些机器学习模型部署到嵌入式系统中，这被称为嵌入式机器学习或TinyML。 建议具备Python编程语言的基础知识以及基本的机器学习概念（如神经网络、训练、推理和评估），以便理解一些主题并完成项目。课程还需要一些数学知识（阅读图表、算术、代数），以用于测验和项目。如果你尚未参加，建议先修“嵌入式机器学习概论”课程。 课程内容包括理解卷积神经网络（CNN）的概念和术语，使用CNN进行图像分类和物体检测。动手项目将为你提供训练自己的CNN并将其部署到微控制器或单板计算机的机会。 课程大纲： 1. **图像分类**：介绍计算机视觉的概念及其解决问题的应用，深入了解数字图像的创建和存储，回顾神经网络及其在简单图像分类中的应用，最后通过项目训练图像分类器并将其部署到嵌入式系统。 2. **卷积神经网络**：讲解卷积神经网络（CNN）的基础知识及其在构建更强大的图像分类模型中的应用，探讨CNN的内部工作原理（如卷积和池化）以及决策可视化技术，引入数据增强的概念以提供更多的训练数据。你将有机会训练自己的CNN并将其部署至嵌入式系统。 3. **物体检测**：讲解物体检测的基本概念及其与图像分类的区别，介绍用于衡量物体检测性能的数学原理，介绍几种流行的物体检测模型，并演示如何在Edge Impulse中训练这些模型，最后要求将物体检测模型部署到嵌入式系统中。
Name:Image Classification
Description: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.
Name:Convolutional Neural Networks
Description: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.
Name:Object Detection
Description: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.
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.
