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Computer Vision with Embedded Machine Learning
  • Level Professional
  • المدة 31 ساعات hours
  • الطبع بواسطة Edge Impulse
  • Offered by

عن

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. This course, offered by a partnership among Edge Impulse, OpenMV, Seeed Studio, and the TinyML Foundation, will give you an understanding of how deep learning with neural networks can be used to classify images and detect objects in images and videos. You will have the opportunity to deploy these machine learning models to embedded systems, which is known as embedded machine learning or TinyML. Familiarity with the Python programming language and basic ML concepts (such as neural networks, training, inference, and evaluation) is advised to understand some topics as well as complete the projects. Some math (reading plots, arithmetic, algebra) is also required for quizzes and projects. If you have not done so already, taking the "Introduction to Embedded Machine Learning" course is recommended. This course covers the concepts and vocabulary necessary to understand how convolutional neural networks (CNNs) operate, and it covers how to use them to classify images and detect objects. The hands-on projects will give you the opportunity to train your own CNNs and deploy them to a microcontroller and/or single board computer.

الوحدات

Introduction

    • 2 Videos
    • 5 Readings
    • 1 Discussion
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1 Discussions

  • Meet and Greet

2 Videos

  • Welcome to the Course
  • Instructor Introductions

5 Readings

  • Syllabus
  • Required Hardware
  • Errata and Changes
  • Getting Help
  • Slides

Introduction to Computer Vision

    • 3 Videos
    • 3 Readings
    • 1 Assignment
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1 Assignment

  • Computer Vision

3 Videos

  • What is Computer Vision?
  • Overview of Digital Images
  • Data Collection

3 Readings

  • Slides
  • Python and Numpy Help
  • Project - Load and Manipulate Images

Image Classification

    • 3 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Image Classification with Neural Networks

3 Videos

  • Overview of Image Classification
  • Review of Neural Networks
  • Training an Image Classifier with Keras

2 Readings

  • Slides
  • Image Classification and Neural Networks

Using Edge Impulse to Train a Model

    • 2 Videos
    • 2 Readings
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2 Videos

  • Using Colab to Curate and Upload a Dataset
  • Using Edge Impulse to Train a Model

2 Readings

  • Python and Edge Impulse Documentation
  • Project - Extract Features and Train Model

Deploy Model to Embedded Device

    • 2 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Image Classification on Embedded Devices

2 Videos

  • Inference on a Single Board Computer
  • Inference on a Microcontroller (MicroPython)

1 Readings

  • Edge Impulse and OpenMV Documentation

Project and Review

    • 1 Videos
    • 2 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Module 1 Review

1 Discussions

  • Share Your Image Classification Project

1 Videos

  • Review of Module 1

2 Readings

  • Project - Deploy DNN Image Classifier
  • Slides

Convolution and Pooling

    • 2 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Convolution and Pooling

2 Videos

  • Image Convolution
  • Pooling Layer

2 Readings

  • Slides
  • Project - Convolution and Pooling

Convolutional Neural Network

    • 2 Videos
    • 3 Readings
    • 1 Assignment
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1 Assignment

  • Convolutional Neural Networks

2 Videos

  • Convolutional Neural Network
  • Training a Convolutional Neural Network

3 Readings

  • Digging Deeper into CNNs
  • Slides
  • Project - Training a CNN

Analyzing and Augmenting CNN Training

    • 2 Videos
    • 3 Readings
    • 1 Assignment
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1 Assignment

  • Visualizations and Data Augmentation

2 Videos

  • CNN Visualizations
  • Data Augmentation

3 Readings

  • Slides
  • CNN Visualizations and Data Augmentation
  • Project - Data Augmentation

Transfer Learning

    • 2 Videos
    • 3 Readings
    • 1 Assignment
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1 Assignment

  • Transfer Learning

2 Videos

  • Transfer Learning and MobileNet
  • Transfer Learning with Edge Impulse

3 Readings

  • Digging Deeper into Transfer Learning
  • Slides
  • Project - Transfer Learning

Project and Review

    • 1 Videos
    • 2 Readings
    • 1 Discussion
    • 1 Assignment
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1 Assignment

  • Module 2 Review

1 Discussions

  • Share Your CNN Classifier Project

1 Videos

  • Review of Module 2

2 Readings

  • Project - Deploy CNN Image Classifier
  • Slides

Object Localization

    • 1 Videos
    • 3 Readings
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1 Videos

  • Introduction to Object Detection

3 Readings

  • Slides
  • Drawing API
  • Project - Sliding Window Object Detection

Creating an Object Detector

    • 3 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Object Detection

3 Videos

  • Object Detection Performance Metrics
  • Object Detection Models
  • Training an Object Detection Model

2 Readings

  • Slides
  • Digging Deeper into Object Detection

Deploying an Object Detector

    • 1 Videos
    • 1 Readings
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1 Videos

  • Deploy Object Detection Model to a Single Board Computer

1 Readings

  • Deploying an Object Detection Model

Going Further

    • 3 Videos
    • 3 Readings
    • 1 Assignment
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1 Assignment

  • Image Segmentation

3 Videos

  • Image Segmentation
  • Multi-stage Inference with Dmitry Maslov
  • Reusing Representations with Mat Kelcey

3 Readings

  • Slides
  • Digging Deeper into Advanced Topics
  • Constrained Object Detection

Project and Review

    • 2 Videos
    • 2 Readings
    • 1 Discussion
    • 1 Plugin
    • 1 Assignment
Show info about module content

1 Assignment

  • Module 3 Review

1 Discussions

  • Share Your Object Detection Model

2 Videos

  • Review of Module 3
  • Conclusion

2 Readings

  • Project - Deploy Object Detection Model
  • Slides

Auto Summary

Dive into the fascinating world of Computer Vision with Embedded Machine Learning, offered through a collaboration among Edge Impulse, OpenMV, Seeed Studio, and the TinyML Foundation. This professional-level course focuses on using deep learning and neural networks to classify images and detect objects, specifically on embedded systems. Ideal for those familiar with Python and basic ML concepts, it combines theoretical knowledge with hands-on projects, allowing you to deploy trained CNNs to microcontrollers. Available on Coursera, the course offers flexible subscription options and is perfect for data science and AI enthusiasts looking to expand their skill set.

Shawn Hymel
Instructor

Shawn Hymel