Artificial Intelligence & Machine Learning
Activation Function
Definition
An activation function is a function used in an artificial neural network that defines the output of a neuron given a set of inputs. It introduces non-linearity into the network, which is crucial for learning complex patterns.
Why It Matters
Without non-linear activation functions, a neural network, no matter how many layers it has, would just behave like a single-layer linear model. Non-linearity is what allows neural networks to learn complex, real-world data.
Contextual Example
Common activation functions include Sigmoid, Tanh, and ReLU (Rectified Linear Unit). The ReLU function, for example, is very simple: it outputs the input directly if it is positive, and outputs zero otherwise. This simple non-linearity has proven to be very effective in deep learning.
Common Misunderstandings
- The activation function is the final step in a neuron's computation before its output is passed on.
- The choice of activation function can have a significant impact on a model's training and performance.