In this 1-hour long project-based course, you will be able to:
- Understand the theory and intuition behind Convolutional Neural Networks (CNNs).
- Import Key libraries, dataset and visualize images.
In this 1-hour long project-based course, you will be able to:
- Understand the theory and intuition behind Convolutional Neural Networks (CNNs).
- Import Key libraries, dataset and visualize images.
- Perform image normalization and convert from color-scaled to gray-scaled images.
- Build a Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend.
- Compile and fit Deep Learning model to training data.
- Assess the performance of trained CNN and ensure its generalization using various KPIs.
- Improve network performance using regularization techniques such as dropout.
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.