Convolutional Layer
In deep learning, a convolutional layer is a type of neural network layer that performs a mathematical operation called convolution on the input data. Convolution is a process of multiplying and summing up the values of a small matrix, called a kernel or filter, with the values of a larger matrix, such as an image. The resulting output matrix is a feature map that highlights the important features of the input data. Convolutional layers are commonly used in image and video recognition tasks, as they can learn to detect patterns and shapes in the input data. They are also used in natural language processing tasks, such as text classification and sentiment analysis, where the input data is represented as a sequence of vectors.
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