The basic building blocks of multilayer perceptrons are known as what?

Multilayer perceptrons, a type of artificial neural network, utilize multiple layers of interconnected nodes to process input data and produce outputs. Within this architecture, hidden units play a critical role in the intermediate layers of the network. These units are responsible for transforming the input received from the previous layer into a form that can be used to produce the final output.

Hidden units apply activation functions to the weighted sum of their inputs, allowing the network to learn complex patterns in the data. They are essential for capturing non-linear relationships and improving the model's ability to generalize from training data to unseen data. The presence of multiple hidden units across various layers enables multilayer perceptrons to create deep learning architectures capable of tackling intricate problems across various domains.

In contrast, input nodes solely serve to receive external data, and output nodes are limited to generating final predictions. While these components are important for the overall function of the network, it is the hidden units that comprise the core elements that facilitate learning and recognition of patterns within the dataset. This is why the hidden units are considered the fundamental building blocks of multilayer perceptrons.