Convolutional Neural Networks

Images are grids, not flat vectors. CNNs exploit spatial locality through shared weights, dramatically reducing parameters while capturing features.

Learning Objectives

• →Understand convolution as a sliding dot product
• →Compute output dimensions given kernel size and stride
• →Implement 2D convolution from scratch
• →Understand max-pooling and its effect on spatial dimensions

Practice