AI Practitioner

  What is a Convolution Layer in Deep Learning? A  convolution layer  is a building block of Convolutional Neural Networks (CNNs). It's mostly used to process  image data . Instead of connecting every pixel of the input to every neuron (as in a fully connected layer), a convolution layer  slides a small filter (kernel)  across the image and  extracts features  like edges, textures, or patterns. Key Terms Input : The image or feature map (e.g., 6x6 pixels). Kernel(Filter) : A small matrix (e.g., 3x3 or 5x5) that moves across the image. Stride : How many steps the filter moves at a time. Padding : Adding extra pixels around the image to control the output size. Feature Map : The result of the convolution operation. How Convolution Works Let’s walk through an example with  no padding  and  stride = 1 . 1. Input: 6x6 Matrix Input: [ [9, 4, 1, 6, 5], [1, 1, 1, 0, 2], [1, 2, 1, 1, 3], [2, 1, 0, 3, 0], [1, 4, 2, 5, 6] ] 2. Kernel: ...