This beginner-friendly course on Convolutional Neural Networks (CNNs) equips you with essential skills to understand deep learning fundamentals and apply them to real-world image recognition tasks. Learn how CNNs power modern AI applications and gain practical experience through guided lab demos. Build confidence in designing, training, and implementing CNN models effectively.
Introduction to CNN Training
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Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
3 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Understand CNN fundamentals and their role in deep learning and computer vision.
Explore key components like convolution, ReLU, and pooling layers.
Learn how CNNs recognize and process images for classification tasks.
Gain hands-on experience building and implementing CNN models through guided labs.
Skills you'll gain
Details to know
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Recently updated!
September 2025
Assessments
6 assignments
Taught in English
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There are 2 modules in this course
Understand the fundamentals of Convolutional Neural Networks (CNNs) to build expertise in deep learning and computer vision. Learn how CNNs recognize images and explore core components like convolution, ReLU, and pooling layers. Gain practical skills through guided lab demos and implement CNN models for real-world image classification tasks.
What's included
8 videos1 reading3 assignments1 plugin
Gain practical experience in Convolutional Neural Networks through step-by-step lab demos. From basics to advanced implementation, this module walks you through multiple hands-on exercises across five demos, helping you build, test, and apply CNN models effectively in real-world scenarios.
What's included
5 videos3 assignments1 plugin
Instructor
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Frequently asked questions
CNN training teaches the fundamentals of Convolutional Neural Networks, including their structure, components, and practical applications in image recognition.
You’ll learn to build, train, and implement CNN models using layers like convolution, ReLU, and pooling for real-world image classification tasks.
Yes, you will receive a certificate upon successfully completing the course and its hands-on projects.
More questions
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