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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/introduction-to-embedded-machine-learning
课程评论：没有评论
课程名称：嵌入式机器学习导论 课程概述： 本课程将介绍机器学习（ML）的基本概念，如何利用数据进行预测和决策，以及如何从经验中学习。近年来，机器学习算法、软件框架和嵌入式硬件得到了显著优化，使得在低功耗设备（如微控制器）上运行深度神经网络及其他复杂机器学习算法成为可能。本课程将提供机器学习的广泛概述，包括如何训练神经网络以及如何将这些网络部署到微控制器上，这一过程被称为嵌入式机器学习或TinyML。参加本课程不需要具备机器学习的先验知识，但建议对Arduino和微控制器有一定的了解，以便理解某些主题并完成项目。同时，课程中会涉及一些数学知识（如阅读图表、算术、代数），用于测验和项目。 课程大纲： 1. **机器学习导论**： 在本模块中，我们将介绍机器学习的概念、如何利用其解决问题以及其局限性。此外，我们会探讨如何在嵌入式系统（如单板计算机和微控制器）上有效地应用机器学习，创造新类型的计算机接口。我们还将介绍Edge Impulse工具，并收集用于“魔法棒”演示的动作数据。最后，我们将审查从原始动作数据中计算出的各种特征，包括均方根（RMS）、傅里叶变换和功率谱密度（PSD）。 2. **神经网络导论**： 在本模块中，我们将研究神经网络的工作原理、如何训练神经网络以及如何在嵌入式系统中进行推理。我们将继续之前的演示，创建一个基于收集的动作数据（来自智能手机或Arduino板）的动作分类系统。最后，我们会给出一个新的动作分类项目挑战，让您有机会实施在本模块及前一模块中学习的概念。 3. **音频分类和关键字识别**： 在本模块中，我们将讨论在嵌入式系统上进行的音频分类，具体包括从录制的音频中提取梅尔频率倒谱系数（MFCC）作为特征、训练卷积神经网络（CNN）并将该神经网络部署到微控制器上。此外，我们还将深入探讨在嵌入式系统上的一些实现策略，并讨论机器学习与传感器融合之间的比较。 通过本课程，学员将掌握机器学习的基本概念与技术，获得动手实践经验，适合希望进入嵌入式机器学习领域的学员。
Name:Introduction to Machine Learning
Description:In this module, we will introduce the concept of machine learning, how it can be used to solve problems, and its limitations. We will also cover how machine learning on embedded systems, such as single board computers and microcontrollers, can be effectively used to solve problems and create new types of computer interfaces. Then, we will introduce the Edge Impulse tool and collect motion data for a "magic wand" demo. Finally, we will examine the various features that can be calculated from this raw motion data, including root mean square (RMS), Fourier transform, and power spectral density (PSD).
Name:Introduction to Neural Networks
Description:In this module, we will look at how neural networks work, how to train them, and how to use them to perform inference in an embedded system. We will continue the previous demo of creating a motion classification system using motion data collected from a smartphone or Arduino board. Finally, we will challenge you with a new motion classification project where you will have the opportunity to implement the concepts learning in this module and the previous module.
Name:Audio classification and Keyword Spotting
Description:In this module, we cover audio classification on embedded systems. Specifically, we will go over the basics of extracting mel-frequency cepstral coefficients (MFCCs) as features from recorded audio, training a convolutional neural network (CNN) and deploying that neural network to a microcontroller. Additionally, we dive into some of the implementation strategies on embedded systems and talk about how machine learning compares to sensor fusion.
Machine learning (ML) allows us to teach computers to make predictions and decisions based on data and learn from experiences. In recent years, incredible optimizations have been made to machine learning algorithms, software frameworks, and embedded hardware. Thanks to this, running deep neural networks and other complex machine learning algorithms is possible on low-power devices like microcontrollers. This course will give you a broad overview of how machine learning works, how to train neural networks, and how to deploy those networks to microcontrollers, which is known as embedded machine learning or TinyML. You do not need any prior machine learning knowledge to take this course. Familiarity with Arduino and microcontrollers is advised to understand some topics as well as to tackle the projects. Some math (reading plots, arithmetic, algebra) is also required for quizzes and projects. We will cover the concepts and vocabulary necessary to understand the fundamentals of machine learning as well as provide demonstrations and projects to give you hands-on experience.
