Visual inspection and medical imaging are two applications that aim to find anything unusual in images. In this course, you’ll train and calibrate specialized models known as anomaly detectors to identify defects. You’ll also use advanced techniques to overcome common data challenges with deep learning. AI-assisted labeling is a technique to auto-label images, saving time and money when you have tens of thousands of images. If you have too few images, you’ll generate synthetic training images using data augmentation for situations where acquiring more data is expensive or impossible.
Advanced Deep Learning Techniques for Computer Vision
Advanced Deep Learning Techniques for Computer Vision
This course is part of Deep Learning for Computer Vision Specialization
Taught in English
Instructors: Mehdi Alemi
Included with 
Course
Gain insight into a topic and learn the fundamentals
Intermediate level
Recommended experience
7 hours (approximately)
Flexible schedule
Learn at your own pace
What you'll learn
Train and calibrate specialized models known as anomaly detectors
Generate synthetic training images for situations where acquiring more data is expensive or impossible
Use AI-assisted auto-labeling to save time and money
Import models from 3rd party tools like PyTorch and export your model outside of MATLAB
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
January 2024
Assessments
7 quizzes
Course
Gain insight into a topic and learn the fundamentals
Intermediate level
Recommended experience
7 hours (approximately)
Flexible schedule
Learn at your own pace
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
This course is part of the Deep Learning for Computer Vision Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
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Share it on social media and in your performance review
There are 4 modules in this course
Train anomaly detection models. These models do not find specific objects or classes, but instead find unusual regions in images.
What's included
4 videos5 readings2 quizzes
Generate synthetic images to use for training models.
What's included
2 videos4 readings2 quizzes
Save hours of manual labor by using model-assisted labeling to prepare images for object detection
What's included
1 video3 readings2 quizzes
Learn how to diagnose problems when training models for your applications. Also, learn the options available to share and use your model outside of MATLAB.
What's included
4 videos4 readings1 quiz1 plugin
Offered by
Recommended if you're interested in Machine Learning
MathWorks
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Frequently asked questions
Yes. A free license is available to learners enrolled in the course. You must have a computer capable of running MATLAB. You can view the system requirements here.
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.