Learning CNNs from Scratch – Visual & Code-Based Guide to Kernels, Convolutions & VGG16 (with Pikachu!)

I’ve been teaching myself computer vision, and one of the hardest parts early on was understanding how Convolutional Neural Networks (CNNs) work—especially kernels, convolutions, and what models like VGG16 actually « see. »

So I wrote a blog post to clarify it for myself and hopefully help others too. It includes:

How convolutions and kernels work, with hand-coded NumPy examples Visual demos of edge detection and Gaussian blur using OpenCV Feature visualization from the first two layers of VGG16 A breakdown of pooling: Max vs Average, with examples

You can view the Kaggle notebook and blog post

Would love any feedback, corrections, or suggestions