What is a "node" in the context of neural networks?

In the context of neural networks, a "node" is defined as a specific computational unit that receives input, processes it through an activation function, and passes the output to subsequent nodes or layers in the network. Each node functions similarly to a neuron in the human brain, taking in weighted inputs, applying a non-linear transformation, and contributing to the overall decision-making process of the network.

Nodes are the building blocks of neural networks, allowing the model to learn and capture complex patterns in the data by adjusting the weights and biases during the training process. This role is crucial for enabling the neural network to make predictions or classifications based on the input data.

In contrast, a network of connections refers to how nodes are interconnected but does not define what a node specifically is. The output layer of a network is a specific part of the architecture that generates the final outputs, already assuming the existence of nodes within it. Lastly, a type of algorithm refers to the methodology or procedure used to train the neural network, which does not encapsulate the definition of what a node is within that network. Therefore, identifying a node as a specific computational unit accurately captures its role within neural networks.