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 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
9 hours (approximately)
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
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
January 2024
Assessments
10 quizzes
Course
Gain insight into a topic and learn the fundamentals
Intermediate level
Recommended experience
9 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
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
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 videos6 readings2 quizzes1 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 videos5 readings3 quizzes
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 readings2 quizzes
Apply your new skills to a final project.
What's included
2 videos2 readings3 quizzes1 plugin
Offered by
Recommended if you're interested in Machine Learning
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MathWorks
Coursera Project Network
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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.