What Convolutional Neural Network (CNN) Meaning, Applications & Example
A neural network architecture well-suited for computer vision tasks.
What is a Convolutional Neural Network (CNN)?
A Convolutional Neural Network (CNN) is a type of deep learning model specifically designed for processing grid-like data, such as images. CNNs use layers with convolutional filters to automatically and adaptively learn spatial hierarchies of features, making them highly effective in image recognition tasks.
Types of Layers in CNNs
- Convolutional Layer: Applies filters to the input, creating feature maps that capture different patterns like edges and textures.
- Pooling Layer: Reduces the spatial dimensions of feature maps, retaining essential information while reducing computational complexity.
- Fully Connected Layer: Connects all neurons, similar to a traditional neural network layer, and is typically used at the end for classification .
Applications of CNNs
- Image Classification: Classifies images into categories, such as distinguishing cats from dogs.
- Object Detection : Locates and identifies multiple objects within an image, widely used in autonomous vehicles and security .
- Medical Imaging: Analyzes X-rays, MRIs, and CT scans to detect diseases or abnormalities with high accuracy.
Example of a Convolutional Neural Network
An example of a CNN is AlexNet, an early and influential model in image classification, which demonstrated breakthrough performance in the 2012 ImageNet competition and paved the way for modern deep learning advancements in computer vision .