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Deep Learning Applications for Computer Vision

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Deep Learning Applications for Computer Vision

Instructor: Ioana Fleming

8,784 already enrolled

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5 modules

Gain insight into a topic and learn the fundamentals.

88 reviews

Intermediate level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

Build toward a degree

Learn more

5 modules

Gain insight into a topic and learn the fundamentals.

88 reviews

Intermediate level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

Build toward a degree

Learn more

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.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

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There are 5 modules in this course

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.

This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder

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 assignment1 discussion prompt

4 videos Total 80 minutes

Lecture 1 22 minutes
Lecture 2 17 minutes
Lecture 3 20 minutes
Lecture 4 21 minutes

13 readings Total 130 minutes

Earn Academic Credit for your Work! 10 minutes
Course Support 10 minutes
Assessment Expectations 10 minutes
What is Computer Vision? 10 minutes
Lecture 1 notes 10 minutes
Lecture 2 notes 10 minutes
Readings and Resources 10 minutes
TED Talk: "How We're Teaching Computers to Understand Pictures" Prof. Fei-Fei Li 10 minutes
Lecture 3 notes 10 minutes
Readings and Resources 10 minutes
Ethics of Driverless Cars - The New Yorker Magazine 10 minutes
Lecture 4 notes 10 minutes
Readings and Resources 10 minutes

1 assignment Total 30 minutes

Computer Vision Areas and Applications 30 minutes

1 discussion prompt Total 10 minutes

Introduce Yourself 10 minutes

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 assignment

5 videos Total 116 minutes

Lecture 5 19 minutes
Lecture 6 18 minutes
Lecture 7 21 minutes
Lecture 8 34 minutes
Lecture 9 23 minutes

10 readings Total 105 minutes

Textbook Readings Modules 2-3 10 minutes
Lecture 5 notes 10 minutes
Lecture 6 notes 10 minutes
Textbook Readings and Other Resources 10 minutes
Lecture 7 notes 10 minutes
Textbook Readings and Other Resources 10 minutes
Lecture 8 notes 10 minutes
Textbook Readings and Other Resources 10 minutes
Lecture 9 notes 10 minutes
Textbook Readings and Other Resources 15 minutes

1 assignment Total 30 minutes

Edge Detection 30 minutes

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 assignment

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

4 videos Total 99 minutes

Lecture 11 29 minutes
Lecture 12 15 minutes
Lecture 13 26 minutes
Lecture 14 29 minutes

5 readings Total 50 minutes

Lecture 11 notes 10 minutes
Lecture 12 notes 10 minutes
Lecture 13 notes 10 minutes
Readings and Resources 10 minutes
Lecture 14 notes 10 minutes

1 peer review Total 90 minutes

Neural Network Parameters 90 minutes

1 ungraded lab Total 60 minutes

Neural Network Parameters 60 minutes

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 assignment1 peer review1 ungraded lab

6 videos Total 112 minutes

Lecture 15 27 minutes
Lecture 16 21 minutes
Lecture 17 24 minutes
Lecture 18 20 minutes
Lecture 19 19 minutes
Conclusion 1 minute

10 readings Total 100 minutes

Lecture 15 notes 10 minutes
Readings and Resources 10 minutes
Lecture 16 notes 10 minutes
Readings and Resources 10 minutes
Lecture 17 notes 10 minutes
Lecture 18 notes 10 minutes
Readings and Resources 10 minutes
Lecture 19 notes 10 minutes
Resources and Readings 10 minutes
Further Resources 10 minutes

1 assignment Total 60 minutes

Final quiz 60 minutes

1 peer review Total 60 minutes

Convolutional Layers 60 minutes

1 ungraded lab Total 60 minutes

Convolutional Layers 60 minutes

Build toward a degree

This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹

Instructor

Instructor ratings

(22 ratings)

Ioana Fleming

University of Colorado Boulder

2 Courses 9,061 learners

Offered by

University of Colorado Boulder

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Reviewed on Jun 16, 2022

Learnt many things and most exciting was Python code part

Reviewed on Jan 2, 2022

Great introductory course on deep learning for computer vision.

Reviewed on Aug 24, 2024

Great content and clear, succinct explanations of the concepts!

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Frequently asked questions

A cross-listed course is offered under two or more CU Boulder degree programs on Coursera. For example, Dynamic Programming, Greedy Algorithms is offered as both CSCA 5414 for the MS-CS and DTSA 5503 for the MS-DS.

· You may not earn credit for more than one version of a cross-listed course.

· You can identify cross-listed courses by checking your program’s student handbook.

· Your transcript will be affected. Cross-listed courses are considered equivalent when evaluating graduation requirements. However, we encourage you to take your program's versions of cross-listed courses (when available) to ensure your CU transcript reflects the substantial amount of coursework you are completing directly in your home department. Any courses you complete from another program will appear on your CU transcript with that program’s course prefix (e.g., DTSA vs. CSCA).

· Programs may have different minimum grade requirements for admission and graduation. For example, the MS-DS requires a C or better on all courses for graduation (and a 3.0 pathway GPA for admission), whereas the MS-CS requires a B or better on all breadth courses and a C or better on all elective courses for graduation (and a B or better on each pathway course for admission). All programs require students to maintain a 3.0 cumulative GPA for admission and graduation.

Yes. Cross-listed courses are considered equivalent when evaluating graduation requirements. You can identify cross-listed courses by checking your program’s student handbook.

You may upgrade and pay tuition during any open enrollment period to earn graduate-level CU Boulder credit for << this course/ courses in this specialization>>. Because << this course is / these courses are >> cross listed in both the MS in Computer Science and the MS in Data Science programs, you will need to determine which program you would like to earn the credit from before you upgrade.

MS in Data Science (MS-DS) Credit: To upgrade to the for-credit data science (DTSA) version of << this course / these courses >>, use the MS-DS enrollment form. See How It Works.

MS in Computer Science (MS-CS) Credit: To upgrade to the for-credit computer science (CSCA) version of << this course / these courses >>, use the MS-CS enrollment form. See How It Works.

If you are unsure of which program is the best fit for you, review the MS-CS and MS-DS program websites, and then contact datascience@colorado.edu or mscscoursera-info@colorado.edu if you still have questions.

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