A Convolutional Neural Network (CNN) is a type of deep learning model specifically designed to process structured grid-like data, such as images and videos. It excels at tasks like image recognition, object detection, and computer vision by mimicking the way the human visual cortex processes visual information.
How It Works:
- Convolution Layers: Apply filters (small matrices) to input data, detecting features such as edges, textures, and shapes.
- Pooling Layers: Reduce the spatial size of the data, retaining essential features while minimizing computational complexity.
- Fully Connected Layers: Combine extracted features to classify or predict outputs, such as identifying objects in an image.
- Backpropagation: Updates the model’s weights through training to improve accuracy based on errors.
Key Features:
- Local Connectivity: Focuses on small regions of data at a time, efficiently identifying patterns.
- Parameter Sharing: Reuses filters across an input, reducing the number of trainable parameters.
- Hierarchy of Features: Builds increasingly abstract representations, from simple edges to complex objects.
Why It Matters:
CNNs are foundational in AI for tasks requiring visual understanding, from facial recognition to autonomous vehicle navigation. Their efficiency and accuracy make them a cornerstone of modern computer vision and deep learning applications.
