How do computers “see” and recognize images?
This session will introduce you to Convolutional Neural Networks (CNNs): the deep learning architecture that powers technologies like facial recognition, autonomous vehicles, and medical image analysis.
You’ll start by understanding why CNNs are essential for image tasks and how they cleverly use filters and feature maps to detect patterns like edges, shapes, and objects.
We’ll demystify the core concepts behind CNNs, explaining kernels, convolutions, pooling, and flattening in a simple and intuitive way.
You’ll use critical Keras layers such as Conv2D, MaxPooling2D, and Flatten, and learn how to design an efficient model architecture.
Duration: 90 mins
Course Code: BDT494
Learning Objectives:
After this course, you will be able to:
Training material provided: Yes (Digital format)
Hands-on Lab: Instructions will be provided to install Jupyter notebook and other required python libraries. Students can opt to use ‘Google Colaboratory’ if they do not want to install these tools
