University of Colorado Boulder - Deep Learning Applications for Computer Vision
- Offered byCoursera
Deep Learning Applications for Computer Vision at Coursera Overview
Duration | 22 hours |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Deep Learning Applications for Computer Vision at Coursera Highlights
- Flexible deadlines Reset deadlines in accordance to the schedule
- Earn a certificate upon completion from Coursera
Deep Learning Applications for Computer Vision at Coursera Course details
- 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
Deep Learning Applications for Computer Vision at Coursera Curriculum
Introduction and Background
Lecture 1
Lecture 2
Lecture 3
Lecture 4
What is Computer Vision?
Lecture 1 notes
Lecture 2 notes
Readings and Resources
TED Talk: "How We're Teaching Computers to Understand Pictures" Prof. Fei-Fei Li
Lecture 3 notes
Readings and Resources
Ethics of Driverless Cars - The New Yorker Magazine
Lecture 4 notes
Readings and Resources
Computer Vision Areas and Applications
Classic Computer Vision Tools
Lecture 5
Lecture 6
Lecture 7
Lecture 8
Lecture 9
Textbook Readings Modules 2-3
Lecture 5 notes
Lecture 6 notes
Textbook Readings and Other Resources
Lecture 7 notes
Textbook Readings and Other Resources
Lecture 8 notes
Textbook Readings and Other Resources
Lecture 9 notes
Textbook Readings and Other Resources
Edge Detection
Image Classification in Computer Vision
Lecture 10: part 1
Lecture 10: part 2
Lecture 10: Part 3
Lecture 10 notes
Textbook Readings and Other Resources
Object Recognition
Neural Networks and Deep Learning
Lecture 11
Lecture 12
Lecture 13
Lecture 14
Lecture 11 notes
Lecture 12 notes
Lecture 13 notes
Readings and Resources
Lecture 14 notes
Convolutional Neural Networks and Deep Learning Advanced Tools
Lecture 15
Lecture 16
Lecture 17
Lecture 18
Lecture 19
Conclusion
Lecture 15 notes
Readings and Resources
Lecture 16 notes
Readings and Resources
Lecture 17 notes
Lecture 18 notes
Readings and Resources
Lecture 19 notes
Resources and Readings
Further Resources
Final quiz
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