Computer vision (CV) is a fascinating field of study that attempts to automate the process of assigning meaning to digital images or videos. In other words, we are helping computers see and understand the world around us! A number of machine learning (ML) algorithms and techniques can be used to accomplish CV tasks, and as ML becomes faster and more efficient, we can deploy these techniques to embedded systems.
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Computer Vision with Embedded Machine Learning
Edge ImpulseÀ propos de ce cours
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Niveau intermédiaire
Some math (reading plots, arithmetic, and algebra) is required in the course. Experience with the Python is recommended to complete the projects.
Approx. 31 heures pour terminer
Anglais
Sous-titres : Anglais
Ce que vous allez apprendre
How to train and develop an image classification system using machine learning
How to train and develop an object detection system using machine learning
How to deploy a machine learning model to a microcontroller
Compétences que vous acquerrez
Machine LearningPython ProgrammingMicrocontrollerEmbedded System DevelopmentComputer 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. Experience with the Python is recommended to complete the projects.
Approx. 31 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 du cours : ce que vous apprendrez dans ce cours
11 heures pour terminer
Image Classification
In this module, we introduce the concept of computer vision and how it can be used to solve problems. We cover how digital images are created and stored on a computer. Next, we review neural networks and demonstrate how they can be used to classify simple images. Finally, we walk you through a project to train an image classifier and deploy it to an embedded system.
11 heures pour terminer
13 vidéos (Total 185 min), 14 lectures, 4 quiz
11 heures pour terminer
Convolutional Neural Networks
In this module, we go over the basics of convolutional neural networks (CNNs) and how they can be used to create a more robust image classification model. We look at the internal workings of CNNs (e.g. convolution and pooling) along with some visualization techniques used to see how CNNs make decisions. We introduce the concept of data augmentation to help provide more data to the training process. You will have the opportunity to train your own CNN and deploy it to an embedded system.
11 heures pour terminer
9 vidéos (Total 114 min), 13 lectures, 5 quiz
9 heures pour terminer
Object Detection
In this module, we will cover the basics of object detection and how it differs from image classification. We will go over the math involved to measure objection detection performance. After, we will introduce several popular object detection models and demonstrate the process required to train such a model in Edge Impulse. Finally, you will be asked to deploy an object detection model to an embedded system.
9 heures pour terminer
10 vidéos (Total 109 min), 10 lectures, 3 quiz
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