What is the term for a collection of nodes in a neural network that transforms input data into output?

The term for a collection of nodes in a neural network that transforms input data into output is referred to as a layer. In the context of a neural network, a layer consists of interconnected nodes (or neurons) that process incoming information and contribute to generating an output. Each layer applies specific transformations to the data it receives, allowing the network to learn complex patterns and relationships in the input data.

Layers can vary in function; for instance, an input layer receives raw data, while hidden layers perform computations, and the output layer delivers the final results. By organizing neurons into layers, neural networks can effectively learn hierarchical representations, influencing the way data is analyzed and predicted. This structure is central to the functioning of deep learning models, where multiple layers allow for increasingly abstract representations of the input data.

In contrast, the other options refer to different aspects of neural network configurations or processes. Neurons are the individual processing units that make up the layers. A matrix typically relates to the way weights are applied in the calculations but does not itself represent a layer. Signal processors refer to hardware or software implementations for processing signals but do not accurately describe a collection of nodes within the context of neural network architecture.