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.
Overview
CNNs consist of layers of neurons that learn to detect and recognize patterns in images. The key features of CNNs include:
- Convolutional Layers: These layers apply various filters to the input image to detect features such as edges, textures, and shapes.
- Pooling Layers: These layers reduce the spatial dimensions of the feature maps, which helps to reduce computational complexity and parameter count.
- Fully Connected Layers: These layers connect all the neurons in the previous layer to all the neurons in the current layer, allowing the network to learn complex patterns.
Example of a CNN Architecture
Here is a simplified example of a CNN architecture:
- Input Layer: The input layer receives the raw image data.
- Convolutional Layer 1: This layer applies filters to the input image to detect edges and textures.
- Pooling Layer 1: This layer reduces the spatial dimensions of the feature maps.
- Convolutional Layer 2: This layer applies more complex filters to detect more complex features.
- Pooling Layer 2: This layer further reduces the spatial dimensions of the feature maps.
- Fully Connected Layer: This layer connects all the neurons from the previous layers to learn complex patterns.
- Output Layer: This layer produces the final prediction, such as the class of the input image.
Further Reading
For more information on CNNs, please refer to the following resources:
