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

Deep Learning Applications for Computer Vision

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

Status: Applied Machine Learning
Status: Computer Vision
IntermediateCourse23 hours

Featured reviews

DM

5.0Reviewed Jun 16, 2022

Learnt many things and most exciting was Python code part

CC

5.0Reviewed Aug 24, 2024

Great content and clear, succinct explanations of the concepts!

JP

5.0Reviewed Jan 2, 2022

G​reat introductory course on deep learning for computer vision.

HT

5.0Reviewed Jan 12, 2025

A well comprehensive course with good explainations for terminologies and concepts in DL

AY

5.0Reviewed Jun 22, 2023

Great Course, The instructor explained the mathematical aspects of the course in a clear manner.

AL

5.0Reviewed Jun 16, 2022

Very good introduction but the practical exercises are so easy.

All reviews

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