University of Colorado Boulder
Deep Learning Applications for Computer Vision
University of Colorado Boulder

Deep Learning Applications for Computer Vision

Taught in English

Some content may not be translated

6,442 already enrolled


Gain insight into a topic and learn the fundamentals

Ioana Fleming

Instructor: Ioana Fleming


(60 reviews)

Intermediate level

Recommended experience

22 hours to complete
3 weeks at 7 hours a week
Flexible schedule
Learn at your own pace
Progress towards a degree

What you'll learn

  • Learners will be able to explain what Computer Vision is and give examples of Computer Vision tasks.

  • Learners will be able to describe the process behind classic algorithmic solutions to Computer Vision tasks and explain their pros and cons.

  • Learners will be able to use hands-on modern machine learning tools and python libraries.

Details to know

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There are 5 modules in this course

In this module, you will learn about the field of Computer Vision. Computer Vision has the goal of extracting information from images. We will go over the major categories of tasks of Computer Vision and we will give examples of applications from each category. With the adoption of Machine Learning and Deep Learning techniques, we will look at how this has impacted the field of Computer Vision.

What's included

4 videos13 readings1 quiz1 discussion prompt

In this module, you will learn about classic Computer Vision tools and techniques. We will explore the convolution operation, linear filters, and algorithms for detecting image features.

What's included

5 videos10 readings1 quiz

In this module we will first review the challenges for object recognition in Classic Computer Vision. Then we will go through the steps of achieving object recognition and image classification in the Classic Computer Vision pipeline.

What's included

3 videos2 readings1 quiz

In this module we will compare how the image classification pipeline with neural networks differs than the one with classic computer vision tools. Then we will review the basic components of a neural network. We will conclude with a tutorial in Tensor flow where we will practice how to build, train and use a neural network for image classification predictions.

What's included

4 videos5 readings1 peer review1 ungraded lab

In this module we will learn about the components of Convolutional Neural Networks. We will study the parameters and hyperparameters that describe a deep network and explore their role in improving the accuracy of the deep learning models. We will conclude with a tutorial in Tensor Flow where we will practice building, training and using a deep neural network for image classification.

What's included

6 videos10 readings1 quiz1 peer review1 ungraded lab


Instructor ratings
4.5 (16 ratings)
Ioana Fleming
University of Colorado Boulder
2 Courses6,695 learners

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Recommended if you're interested in Machine Learning

Get a head start on your degree

This course is part of the following degree programs offered by University of Colorado Boulder. If you are admitted and enroll, your coursework can count toward your degree learning and your progress can transfer with you.

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