A Beginner’s Guide to Understanding the Layers of a Convolutional Neural Network
Are you interested in learning about convolutional neural networks (CNNs), but feel a little intimidated by all the technical jargon? Don’t worry, you’re not alone! In this article, we’ll take a deep dive into the layers of a CNN and how they work together to process and analyze images. Using a metaphor to help illustrate the concepts, we’ll break down each layer in a way that’s easy to understand and enjoyable to read. So sit back, relax, and let’s demystify the mysterious world of CNNs together.
Imagine that you are a detective trying to solve a crime. The input layer of a CNN is like the evidence you have collected from the crime scene. It consists of raw pixel values from an image that you will use to try and figure out what happened.
The next layer is the convolutional layer, which is like the magnifying glass you use to examine the evidence more closely. This layer is responsible for extracting features from the input image, such as edges and shapes. To do this, it uses a small matrix called a kernel or filter that it slides across the image, looking for patterns. These patterns are then turned into a feature map, which is passed through an activation function to introduce non-linearity to the network. This is important because it allows the CNN to model more complex…
