What is a convolutional operation?
The following two-step mathematical operation: 1.
convolutional operation explained in plain English
The following two-step mathematical operation: 1. Element-wise multiplication of the convolutional filter and a slice of an input matrix. (The slice of the input matrix has the same rank and size as the convolutional filter.) 2. Summation of all the values in the resulting product matrix. For example, consider the following 5x5 input matrix: Now imagine the following 2x2 convolutional filter: Each convolutional operation involves a single 2x2 slice of the input matrix. For example, suppose we use the 2x2 slice at the top-left of the input matrix. So, the convolution operation on this slice looks as follows: A convolutional layer consists of a series of convolutional operations, each acting on a different slice of the input matrix.
Example
Practitioners refer to convolutional operation when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
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- convolution
In mathematics, casually speaking, a mixture of two functions.
- convolutional filter
One of the two actors in a convolutional operation.
- convolutional layer
A layer of a deep neural network in which a convolutional filter passes along an input matrix.
- stride
In a convolutional operation or pooling, the delta in each dimension of the next series of input slices.
- autoencoder
A system that learns to extract the most important information from the input.
- auxiliary loss
A loss function—used in conjunction with a neural network model's main loss function—that helps accelerate training during the early iterations when weights are randomly initialized.
- Backpropagation
The process that tells a neural network which internal settings caused an error and how to adjust them, working backwards through layers.
- Bayesian neural network
A probabilistic neural network that accounts for uncertainty in weights and outputs.
- convex function
A function in which the region above the graph of the function is a convex set.
- convolutional neural network
A neural network in which at least one layer is a convolutional layer.