Convolutional Neural Network (CNN)
Architecture specialized for grid-like data (images). Uses filters (kernels) to detect local features (edges, textures) hierarchically.
Key Concepts
Convolutional Layers
The core building block of a CNN.
Pooling Layers
Used to reduce the dimensionality of the feature maps.
Fully Connected Layers
Used to classify the input image.
Detailed Explanation
A convolutional neural network (CNN) is a type of artificial neural network that is well-suited for computer vision tasks. CNNs are composed of a series of convolutional and pooling layers, which are used to extract features from images.
The convolutional layers in a CNN are responsible for detecting local features in an image, such as edges and textures. The pooling layers are responsible for reducing the dimensionality of the feature maps, which helps to make the network more efficient. The fully connected layers are responsible for classifying the input image.
Real-World Examples & Use Cases
Image Classification
CNNs are used to classify images into different categories.
Object Detection
CNNs are used to identify and locate objects within an image.
Semantic Segmentation
CNNs are used to classify each pixel in an image.