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所在平台: Udemy |
课程主页: https://www.udemy.com/course/intro-to-embedded-ml/
课程评论：没有评论
课程名称：嵌入式机器学习入门 课程概述：本课程将带您深入了解嵌入式机器学习这一领域。近年来，嵌入式系统的技术进步使微控制器能够运行复杂的机器学习模型。用于机器学习应用的嵌入式设备在工业中能够完成多种任务。一个典型的例子是：传感器设备可以检测声学或光学异常和差异，从而支持生产的质量保证或系统状态监测。这些设备不仅使用相机监测视觉参数，使用麦克风记录声波，还利用传感器检测振动、接触、电压、电流、速度、压力和温度。 虽然关于嵌入式系统和机器学习的教育内容已相对丰富，但关于嵌入式机器学习的教育内容尚未跟上。本课程旨在填补这一空白，提供嵌入式系统、机器学习和小型机器学习（Tiny ML）的基础知识。课程最后将通过一个互动项目进行总结，学习者将有机会创建自己的专属嵌入式机器学习项目。该项目将基于使用微控制器或个人移动设备进行声学事件检测。到课程结束时，您将能够选择自己的分类和音频，并自行训练和部署机器学习模型。这是让您了解并获取嵌入式机器学习领域宝贵经验的绝佳途径。
In this course, you will learn more about the field of embedded machine learning. In recent years, technological advances in embedded systems have enabled microcontrollers to run complicated machine learning models. Embedded devices for machine learning applications can fulfill many tasks in the industry. One typical example: sensor devices that detect acoustic or optical anomalies and discrepancies and, in this way, support quality assurance in production or system condition monitoring. In addition to cameras for monitoring visual parameters and microphones for recording soundwaves, these devices also use sensors for, for instance, vibration, contact, voltage, current, speed, pressure, and temperature.Even though there is plenty of educational content on embedded systems and machine learning individually, educational content on embedded ML has yet to catch up. This course attempts to fill that void by providing fundamentals of embedded systems, machine learning, and Tiny ML. This course will conclude with an interactive project where the learner will get to create their own specialized embedded ML project. This project will be based on acoustic event detection using a microcontroller or your own mobile device. By the end of the course, you will be able to pick your own classifications and audio and train and deploy a machine learning model yourself. This is a great way to introduce yourself to and gain valuable experience in the field of embedded machine learning.
