|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/tinyml-with-arduino-nano-rp2040-connect/
课程评论：没有评论
课程概述：TinyML与Arduino Nano RP2040 Connect 本课程旨在介绍TinyML的发展，尤其是其在Arduino Nano RP2040 Connect上应用的实际操作。TinyML是一个快速发展的机器学习领域，专注于为电池供电的设备提供低功耗的智能分析。它通过集成电路、算法和软件，实现对传感器数据的现场处理，通常耗电量在毫瓦范围及以下，从而消除了将数据传输到云端进行分类的必要，增强了数据安全性。 在本课程中，学员将学习如何使用一款低成本的Arduino Nano RP2040 Connect开发板，该开发板配备了265KB的RAM和16MB的闪存，内置加速度传感器、陀螺仪、麦克风、温度传感器以及无线连接模块（WiFi+Bluetooth）。课程内容涵盖数据收集、模型训练、测试及部署等环节，旨在使学员熟悉TinyML的开发流程。 尽管TinyML具有一些限制，例如硬件资源有限和时钟速度较慢，但在许多不需要高计算能力的应用领域，机器学习解决方案依然渴望被采用，包括工厂设备故障检测、仪器的维护需求预测以及医疗健康等领域。TinyML的应用前景广阔，未来发展乐观。 需要注意的是，本课程尚未最终确定，未来将增加更多理论讲解和动手项目的内容，以满足持续发展的TinyML领域需求。
**Note: This course is not finalized yet. As you know, the TinyML field is constantly growing and developing. So, keeping in mind more sections with theoretical explanations with hands-on project ideas will be included in the near future.Tiny machine learning, which targets battery-operated devices, is broadly defined as a rapidly expanding field of machine learning technologies and applications that includes hardware (dedicated integrated circuits), algorithms, and software that can perform on-device sensor data analytics at extremely low power, typically in the mW range and below. It eliminates the requirement to send data to the cloud for classification thus providing more security. Also, power-hungry processors are being replaced by a tiny MCU. Of course, there are limitations. The limitations came from limited hardware resources, clock speed, etc. Still, there are several application areas where high computation is not required and a machine learning-based solution is desirable. In that case, TinyML will come into the picture. It can be used to detect anomalies in machinery in a factory, it can predict maintenance requirements of the instruments, healthcare field, and so on. The application domain of TinyML is wide and the future is bright.The primary objective of this course is to be familiar with TinyML development starting from data collection, model training, testing, and deployment. A low-cost Arduino nano RP2040 connect board having 265KB RAM and 16MB flash with in built accelerometer, Gyroscope, Microphone, temperature sensor, and wireless connectivity module (WiFi+Bluetooth) is used in this course and all example demonstrated here is tested on this board.
