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.
Deep Learning Applications for Computer Vision
Deep Learning Applications for Computer Vision
Instructor: Ioana Fleming
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Gain insight into a topic and learn the fundamentals.
4.6
(76 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 22 hours
Learn at your own pace
Build toward 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.
Skills you'll gain
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Assessments
4 quizzes
Taught in English
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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
Instructor ratings
4.5 (19 ratings)Offered by
Recommended if you're interested in Machine Learning
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
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4.6
76 reviews
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Showing 3 of 76
CC
5
Reviewed on Aug 24, 2024
Great content and clear, succinct explanations of the concepts!
AL
5
Reviewed on Jun 16, 2022
Very good introduction but the practical exercises are so easy.
AY
5
Reviewed on Jun 22, 2023
Great Course, The instructor explained the mathematical aspects of the course in a clear manner.
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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.
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