Introduction to Deep Learning for Computer Vision
Learn deep learning fundamentals for computer vision: train and optimize neural networks for image classification.
Learn deep learning fundamentals for computer vision: train and optimize neural networks for image classification.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Deep Learning for Computer Vision Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
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What you'll learn
Develop strong foundations in deep learning for image analysis
Retrain common models like GoogLeNet and ResNet
Investigate and optimize model behavior
Implement complete deep learning workflows
Skills you'll gain
This course includes:
1.2 Hours PreRecorded video
9 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 4 modules in this course
Starting with zero deep learning knowledge, this foundational course guides students through training cutting-edge models for image classification. The curriculum covers CNN fundamentals, transfer learning with pre-trained models like GoogLeNet and ResNet, and model optimization techniques. Through hands-on projects involving traffic sign classification and American Sign Language recognition, students develop practical skills in implementing the complete deep learning workflow.
Introduction to Deep Learning with Images
Module 1 · 3 Hours to complete
Transfer Learning
Module 2 · 2 Hours to complete
Investigating Network Behavior
Module 3 · 1 Hours to complete
Final Project: Classifying the ASL Alphabet
Module 4 · 2 Hours to complete
Fee Structure
Instructors
Manager Online Courses
Brandon Armstrong is a Principal Online Content Developer at MathWorks. He earned a Ph.D. in physics from the University of California at Santa Barbara in 2010.
Online Course Developer at MathWorks
Amanda Wang is an Online Course Developer at MathWorks, specializing in creating educational content related to MATLAB and its applications in computer vision and deep learning. She holds dual Bachelor's degrees in Mathematics with Computer Science and Business Analytics from the Massachusetts Institute of Technology (MIT), which she completed in 2020. Currently, Amanda is pursuing a Master’s degree in Computer Science from the University of Illinois Urbana-Champaign.
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Frequently asked questions
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