Convolutional Neural Networks (CNNs)

Imagine you are trying to teach a computer to recognize a cat. You could describe rules: pointy ears, whiskers, fur. But what about a cat curled up in a ball, or photographed from behind, or partially hidden behind a dog? The truth is that you can't write enough rules to cover all possibilities. And yet, in the time it took me to say that sentence, a convolutional neural network could have correctly identified thousands of images of cats it had never seen before. This is the power of CNNs, and this is what we will understand here.

If you have worked with neural networks before, you may have used CNNs as out-of-the-box tools. You import a pre-trained model, feed it images, and get predictions as output — and it works, until it doesn't. Your model might suddenly start performing poorly, and you will have no idea why. You then realize that you have treated your model like a black box. This course is about opening that box.

We'll start by understanding why CNNs exist in the first place — what limitations of traditional neural networks were they designed to address? Next, we'll break down the building blocks: convolution layers, pooling operations, and how these components work togethe...