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.
Computer Vision with Embedded Machine Learning
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Computer Vision with Embedded Machine Learning
Instructor: Shawn Hymel
21,989 already enrolled
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4.8
(134 reviews)
Intermediate level
Recommended experience
30 hours to complete
3 weeks at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
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
Skills you'll gain
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Assessments
12 assignments
Taught in English
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There are 3 modules in this course
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.
What's included
13 videos15 readings4 assignments2 discussion prompts
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.
What's included
9 videos13 readings5 assignments1 discussion prompt
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.
What's included
10 videos11 readings3 assignments1 discussion prompt1 plugin
Instructor
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4.8 (41 ratings)Offered by
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