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.
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Introduction to Embedded Machine Learning
Edge ImpulseÀ propos de ce cours
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Certificat partageable
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100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Niveau intermédiaire
Some math (reading plots, arithmetic, and algebra) is required in the course. Recommended to have experience with embedded systems (e.g. Arduino).
Approx. 17 heures pour terminer
Anglais
Sous-titres : Anglais
Ce que vous allez apprendre
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
Compétences que vous acquerrez
- Arduino
- Machine Learning
- Embedded System Design
- Microcontroller
- Computer Programming
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Niveau intermédiaire
Some math (reading plots, arithmetic, and algebra) is required in the course. Recommended to have experience with embedded systems (e.g. Arduino).
Approx. 17 heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
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.
Programme de cours : ce que vous apprendrez dans ce cours
6 heures pour terminer
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).
6 heures pour terminer
13 vidéos (Total 107 min), 14 lectures, 5 quiz
6 heures pour terminer
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.
6 heures pour terminer
10 vidéos (Total 77 min), 10 lectures, 5 quiz
6 heures pour terminer
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.
6 heures pour terminer
9 vidéos (Total 75 min), 7 lectures, 4 quiz
Avis
- 5 stars82,94 %
- 4 stars14,73 %
- 3 stars2,02 %
- 1 star0,28 %
Meilleurs avis pour INTRODUCTION TO EMBEDDED MACHINE LEARNING
par MC14 déc. 2021
It was a good start for those who do not have prior knowledge on Machine Learning. It was a motivating course.
par AP19 avr. 2022
i like the way course is designed. i tried all project explained in course without re-viewing cource material.
par TN14 mars 2021
The videos and supplemental materials were well-presented and very useful. The hands-on projects were the best for learning practical use of the concepts.
par SK10 févr. 2022
Quite good introduction into the topic of ML and the limits caused by embedded HW. Thank you!
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