Convolutional Neural Networks (CNNs) are a class of deep neural networks that are particularly effective for analyzing visual imagery. They are widely used in computer vision tasks such as image classification, object detection, and image segmentation.

CNN Architecture

A typical CNN architecture consists of several layers:

  • Input Layer: This layer receives the input image.
  • Convolutional Layers: These layers apply various filters to the input image to extract features.
  • Pooling Layers: These layers reduce the spatial dimensions of the feature maps.
  • Fully Connected Layers: These layers perform classification based on the extracted features.

Key Concepts

  • Convolution: The process of applying filters to the input image to extract features.
  • Activation Function: A function that introduces non-linearity into the network.
  • Pooling: A technique used to reduce the spatial dimensions of the feature maps.

Example

Here's an example of a CNN architecture:

Input Layer -> Convolutional Layer -> Activation Function -> Pooling Layer -> Convolutional Layer -> Activation Function -> Pooling Layer -> Fully Connected Layer -> Output Layer

Resources

For more information on CNNs, you can refer to the following resources:

CNN Architecture