What benefit does a multi-layer perceptron offer over a simple perceptron?

A multi-layer perceptron (MLP) provides significant advantages over a simple perceptron, particularly in its ability to model complex patterns in data. The correct answer emphasizes this capability: an MLP can classify non-linear relationships, which a simple perceptron cannot achieve.

A simple perceptron is limited to solving linear classification problems because it consists of only a single layer of output nodes connected directly to input features. It uses a linear decision boundary to separate classes, which restricts its usefulness for complex datasets where the relationships between inputs and outputs are not straightforward and linear.

In contrast, a multi-layer perceptron, featuring one or more hidden layers with multiple neurons, utilizes non-linear activation functions. This structure allows the MLP to learn and represent intricate relationships in data, making it possible to tackle tasks such as image classification, natural language processing, and other complex problems where data is not linearly separable.

Understanding this distinction is crucial for effectively leveraging neural networks in various machine learning applications. The other options do not accurately represent the capabilities of a multi-layer perceptron in comparison to a simple perceptron. For example, while a simple perceptron does indeed accept only binary inputs, this limitation does not pertain to the