What is a convolutional layer?
A layer of a deep neural network in which a convolutional filter passes along an input matrix.
convolutional layer explained in plain English
A layer of a deep neural network in which a convolutional filter passes along an input matrix. For example, consider the following 3x3 convolutional filter: The following animation shows a convolutional layer consisting of 9 convolutional operations involving the 5x5 input matrix. Notice that each convolutional operation works on a different 3x3 slice of the input matrix. The resulting 3x3 matrix (on the right) consists of the results of the 9 convolutional operations:
Example
Practitioners refer to convolutional layer 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 operation
The following two-step mathematical operation: 1.
- 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.