Intermediate · Neural Networks
Convolution
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A mathematical operation that applies a filter to an input matrix, used in deep learning to extract features efficiently.
Technical Definition
A mathematical operation that applies a filter to an input matrix, used in deep learning to extract features efficiently.
How it works
In machine learning, a convolution is a fundamental operation, typically performed in convolutional layers, that combines a small matrix called a filter with an input matrix (like an image). The filter slides across the input, performing element-wise multiplication and summation at each position to produce an output feature map. This process allows models to learn spatial hierarchies of features without needing to learn a separate weight for every single pixel.
Related Concepts
- Feature Extraction — Deriving informative numerical signals from raw data for use as model inputs.
- Convolutional filter — A small matrix used in convolutional operations to detect specific features like edges or textures within an input matrix.
- Convolutional layer — A layer in a neural network that applies convolutional filters across the input to create feature maps, commonly used in image processing.