• Convolution Layer
• A simple CNN Architecture
• Another simple CNN architecture
• References

Convolution Layer

The convolution layer is the first layer in a Convolutional Neural Network (CNN). It is responsible for extracting features from the input image. The convolution layer consists of a set of filters that are convolved with the input image to produce feature maps. Each filter is a small matrix that is applied to the input image using a sliding window. The filter is moved across the input image, and at each position, the dot product of the filter and the input image is computed to produce a single value in the feature map. This process is repeated for each filter in the convolution layer to produce multiple feature maps.

A simple CNN Architecture

Another simple CNN architecture

References

• StatQuest: Neural Networks Part 8: Image Classification with Convolutional Neural Networks (CNNs)
• Andrew Ng’s C4W1L10 CNN Example