Mastering the Mathematics Behind CNN or Convolutional Neural Networks in Deep Learning – Day 54

You have seen what’s CNN in our previous article:View Article.Now let’s check the mathematics behind in details & step by step with a very simple example.   Part 1: Input Layer, Convolution, and Pooling (Steps 1-4) Step 1: Input Layer We are processing two 3×3 grayscale images—one representing a zebra and one representing a cat. Image 1: Zebra Image (e.g., with stripe-like patterns) Image 2: Cat Image (e.g., with smoother, fur-like textures) These images are represented as 2D grids of pixel values, with each value between 0 and 1 indicating pixel intensity. Step 2: Convolutional Layer (Feature Extraction) We’ll apply a 3×3 convolutional filter to detect patterns such as edges. For simplicity, we’ll use the same filter for both images. Convolution Filter (Edge Detector): Convolution on the Zebra Image For the first patch (the full 3×3 grid), the element-wise multiplication with the filter is: Summing the values: The feature map value for this part of the zebra image is 0.7. Convolution on the Cat Image Now, let’s perform the convolution on the cat image. Summing the values: The feature map value for this part of the cat image is -0.3. Step 3: ReLU Activation (Non-Linearity) The ReLU activation function converts…