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.
제공자:
Introduction to Embedded Machine Learning
Edge Impulse이 강좌에 대하여
최근 조회 42,163회
유연한 마감일
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공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
중급 단계
Some math (reading plots, arithmetic, and algebra) is required in the course. Recommended to have experience with embedded systems (e.g. Arduino).
완료하는 데 약 17시간 필요
영어
자막: 영어
배울 내용
The basics of a machine learning system
How to deploy a machine learning model to a microcontroller
How to use machine learning to make decisions and predictions in an embedded system
귀하가 습득할 기술
- Arduino
- Machine Learning
- Embedded System Design
- Microcontroller
- Computer Programming
유연한 마감일
일정에 따라 마감일을 재설정합니다.
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
중급 단계
Some math (reading plots, arithmetic, and algebra) is required in the course. Recommended to have experience with embedded systems (e.g. Arduino).
완료하는 데 약 17시간 필요
영어
자막: 영어
제공자:
Edge Impulse
Edge Impulse is the leading development platform for machine learning on edge devices, free for developers and trusted by enterprises. Founded in 2019 by Zach Shelby and Jan Jongboom, we are on a mission to enable developers to create the next generation of intelligent devices. We believe that machine learning can enable positive change in society, and we are dedicated to support applications for good.
강의 계획표 - 이 강좌에서 배울 내용
완료하는 데 6시간 필요
Introduction to Machine Learning
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).
완료하는 데 6시간 필요
13개 동영상 (총 107분), 14 개의 읽기 자료, 5 개의 테스트
완료하는 데 6시간 필요
Introduction to Neural Networks
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.
완료하는 데 6시간 필요
10개 동영상 (총 77분), 10 개의 읽기 자료, 5 개의 테스트
완료하는 데 6시간 필요
Audio classification and Keyword Spotting
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.
완료하는 데 6시간 필요
9개 동영상 (총 75분), 7 개의 읽기 자료, 4 개의 테스트
검토
- 5 stars82.99%
- 4 stars14.69%
- 3 stars2.01%
- 1 star0.28%
INTRODUCTION TO EMBEDDED MACHINE LEARNING의 최상위 리뷰
EL 제공2021년 2월 27일
A great introduction to tinyML and embedded machine learning using Edge Impulse to get started working on projects immediately.
SJ 제공2021년 8월 18일
Awesome course for beginners. I don't know how much of my background helped make this awesome, but it is awesome.
SS 제공2021년 4월 10일
Short and sweet course. A very lucid introduction to the beautiful world of TinyML. Thanks.
SA 제공2021년 2월 17일
ML on embedded systems is an exciting and growing technology and business domain. This course is a great place to start learning about it.
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