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Deep Learning Applications for Computer Vision
  • Level Professional
  • المدة 23 ساعات hours
  • الطبع بواسطة University of Colorado Boulder
  • Offered by

عن

This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. In this course, you’ll be learning about Computer Vision as a field of study and research. First we’ll be exploring several Computer Vision tasks and suggested approaches, from the classic Computer Vision perspective. Then we’ll introduce Deep Learning methods and apply them to some of the same problems. We will analyze the results and discuss advantages and drawbacks of both types of methods. We'll use tutorials to let you explore hands-on some of the modern machine learning tools and software libraries. Examples of Computer Vision tasks where Deep Learning can be applied include: image classification, image classification with localization, object detection, object segmentation, facial recognition, and activity or pose estimation.

الوحدات

Course Introduction

    • 1 Videos
    • 5 Readings
    • 1 Discussion
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1 Discussions

  • Introduce Yourself

1 Videos

  • Lecture 1

5 Readings

  • Earn Academic Credit for your Work!
  • Course Support
  • Assessment Expectations
  • What is Computer Vision?
  • Lecture 1 notes

Computer Vision Areas: Motion Analysis

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 2

2 Readings

  • Lecture 2 notes
  • Readings and Resources

Neural Networks and Their Impact in Computer Vision

    • 1 Videos
    • 3 Readings
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1 Videos

  • Lecture 3

3 Readings

  • TED Talk: "How We're Teaching Computers to Understand Pictures" Prof. Fei-Fei Li
  • Lecture 3 notes
  • Readings and Resources

Deep Learning for Computer Vision - Data Challenges

    • 1 Videos
    • 3 Readings
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1 Videos

  • Lecture 4

3 Readings

  • Ethics of Driverless Cars - The New Yorker Magazine
  • Lecture 4 notes
  • Readings and Resources

Assessments

    • 1 Quiz
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1 Quiz

  • Computer Vision Areas and Applications

What is an Image? Image Features

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 5

2 Readings

  • Textbook Readings Modules 2-3
  • Lecture 5 notes

Linear Filters, Convolution

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 6

2 Readings

  • Lecture 6 notes
  • Textbook Readings and Other Resources

Gradients and Linear Filters

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 7

2 Readings

  • Lecture 7 notes
  • Textbook Readings and Other Resources

An Algorithm for Edge Detection

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 8

2 Readings

  • Lecture 8 notes
  • Textbook Readings and Other Resources

Texture

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 9

2 Readings

  • Lecture 9 notes
  • Textbook Readings and Other Resources

Assessments

    • 1 Quiz
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1 Quiz

  • Edge Detection

From Object Recognition to Image Classification in Classic Computer Vision

    • 3 Videos
    • 2 Readings
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3 Videos

  • Lecture 10: part 1
  • Lecture 10: part 2
  • Lecture 10: Part 3

2 Readings

  • Lecture 10 notes
  • Textbook Readings and Other Resources

Assesments

    • 1 Quiz
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1 Quiz

  • Object Recognition

Object Recognition and Image Classification with Neural Networks

    • 1 Videos
    • 1 Readings
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1 Videos

  • Lecture 11

1 Readings

  • Lecture 11 notes

Neural Networks for Image Classification

    • 1 Videos
    • 1 Readings
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1 Videos

  • Lecture 12

1 Readings

  • Lecture 12 notes

The Image Classification Pipeline

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 13

2 Readings

  • Lecture 13 notes
  • Readings and Resources

Neural Networks Tutorial - Tensor Flow

    • 1 Videos
    • 1 Readings
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1 Videos

  • Lecture 14

1 Readings

  • Lecture 14 notes

Assessments

    • 1 PeerReview
    • 1 Lab
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1 Peer Review

  • Neural Network Parameters

1 Labs

  • Neural Network Parameters

Convolutional Neural Networks

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 15

2 Readings

  • Lecture 15 notes
  • Readings and Resources

More Hyperparameters and Pooling Layers

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 16

2 Readings

  • Lecture 16 notes
  • Readings and Resources

CNN Tutorial

    • 1 Videos
    • 1 Readings
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1 Videos

  • Lecture 17

1 Readings

  • Lecture 17 notes

Visualizing CNNs

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 18

2 Readings

  • Lecture 18 notes
  • Readings and Resources

Deep Learning Networks - other considerations

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 19

2 Readings

  • Lecture 19 notes
  • Resources and Readings

Assesment

    • 1 PeerReview
    • 1 Lab
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1 Peer Review

  • Convolutional Layers

1 Labs

  • Convolutional Layers

Final Assessment

    • 1 Quiz
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1 Quiz

  • Final quiz

Conclusion

    • 1 Videos
    • 1 Readings
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1 Videos

  • Conclusion

1 Readings

  • Further Resources

Auto Summary

"Deep Learning Applications for Computer Vision" is a professional course in Data Science & AI, instructed by Coursera. It focuses on Computer Vision tasks, comparing classic and Deep Learning methods. Learners will explore image classification, object detection, and more through hands-on tutorials using modern machine learning tools. With a duration of 1380 minutes, the course is part of CU Boulder’s MS in Data Science and Computer Science degrees on Coursera, featuring short sessions and pay-as-you-go tuition. Ideal for recent graduates and professionals, it offers Starter and Professional subscription options.

Ioana Fleming
Instructor

Ioana Fleming