Activation Function Selection

Published:

Activation functions control how a neuron in a neural network transforms its input into an output. They give the network the ability to learn complex patterns. Without them, a neural network would only be able to learn linear relationships. Activation functions introduce the non-linearity needed for tasks like image recognition, language understanding, and many others.

Choosing an activation function affects how information flows through the network and how easily it can be trained. Different functions behave differently during training. Some help gradients move smoothly through the network, while others can slow learning or cause issues like vanishing gradients. The right choice depends on the task, architecture, and data.

Follow us on Facebook and LinkedIn to keep abreast of our latest news and articles