What characteristic is true about Activation Functions?

The correct answer highlights the importance of computational efficiency in activation functions since they are applied across numerous nodes in a neural network. In practice, neural networks can contain many layers and a substantial number of neurons, where each neuron uses an activation function to produce its output. Given the scale of calculations involved, where potentially millions of operations may occur, efficient computation is crucial to ensure quick processing and training times.

Activation functions are fundamental in introducing non-linearity to the model, enabling it to learn complex patterns. Using functions that are computationally efficient helps maintain performance, especially when deploying models in real-time scenarios or on devices with limited processing power.

Choosing the proper activation function can lead to faster convergence during training and more generalizable models. Functions like ReLU (Rectified Linear Unit) are particularly favored for their simple computation, while others like sigmoid or tanh can be computationally heavier.

In contrast, other options mentioned do not encapsulate this distinct aspect. While applying a mathematical function to an input vector is a function of activation, it does not reflect the necessity for computational efficiency. The description of an input node including an activation function conflates concepts of architecture with those of operation efficiency. Additionally, the claim that only ReLU and sigmoid are types of activation functions