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Convolutional Neural Network (CNN)

What is a Convolutional Neural Network (CNN)?

A Convolutional Neural Network (CNN) is a type of artificial neural network designed to process structured grid data, such as an image. CNNs are particularly effective for tasks like image and video recognition, as they can capture spatial features from the input data.

How does a CNN work?

A CNN works by applying a series of filters to the input data. These filters, or convolutional layers, can detect local patterns, such as edges or textures. The output of these layers is then passed through non-linear activation functions and pooled to reduce dimensionality. This process is repeated multiple times, with each layer capturing more complex features.

The final layers of the CNN are typically fully connected and output a prediction for the task at hand.

What are the advantages of CNNs?

CNNs have several advantages. They are invariant to the position of features in the input data, meaning they can recognize patterns regardless of their location in the image. They also require fewer parameters than fully connected networks, making them more efficient to train.

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