Starting with zero deep learning knowledge, this foundational course will guide you to effectively train cutting-edge models for image classification purposes. From analyzing medical images to recognizing traffic signs, classification is important for many applications. Classification models also serve as the backbone for more complicated object detection models. Through hands-on projects, you will train and evaluate models to classify street signs and identify the letters of American Sign Language. By completing this course, you will develop a strong foundation in deep learning for image analysis and will be equipped with the skills to tackle real-world computer vision challenges.
Introduction to Deep Learning for Computer Vision
Introduction to Deep Learning for Computer Vision
This course is part of multiple programs.
Instructors: Mehdi Alemi
2,559 already enrolled
Included with 
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
9 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Develop a strong foundation in deep learning for image analysis
Retrain common models like GoogLeNet and ResNet for specific applications
Investigate model behavior to identify errors, determine potential fixes, and improve model performance
Complete a real-world project to practice the entire deep learning workflow
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
9 assignments
Taught in English
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This course is available as part of
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- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
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There are 4 modules in this course
Learn the key components of convolutional neural networks and train a simple classification model
What's included
5 videos7 readings2 assignments1 discussion prompt
Retraining networks with new data is the most common way to apply deep learning in industry. In this module, you'll retrain common networks, set appropriate values for training options, and compare results from different models.
What's included
4 videos6 readings3 assignments
Explaining how models make predictions is increasingly important. In this module, you'll use confidence scores and visualizations to determine what regions of an image the model is using to make predictions. You'll also identify common errors and adjust training options to improve performance.
What's included
2 videos2 readings1 assignment
Apply your new skills to a final project.
What's included
2 videos2 readings3 assignments1 plugin
Offered by
Recommended if you're interested in Machine Learning
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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.
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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 Certificate, 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.