A convolutional neural network (CNN) is a type of artificial neural network that uses convolutional layers to explore the spatial relationships in the input data. Unlike regular neural networks, which are fully connected and accept input vectors of any length, CNNs have a restricted connectivity pattern and operate on fewer parameters. This makes them well-suited for processing complex, multi-dimensional inputs such as images. Convolutional layers allow the network to identify different features in the input data and extract them for further transformation and analysis. The layers are arranged in a hierarchical structure, with each layer taking the output of the previous layer as its input and transforming it to produce the next output. This hierarchical structure allows the network to learn representations of the input data at different levels of abstraction, allowing it to identify increasingly complex features in the data.
Explain what a "convoluted neural network" is and how it differs from a regular neural network.