Neural Network Activation Functions

- Overview

A neural network activation function is a mathematical function that determines the output of a neuron in a neural network based on its input, essentially deciding whether the neuron should be "activated" or not, allowing the network to learn complex patterns by introducing non-linearity into the model; without them, neural networks would only be able to model linear relationships between inputs and outputs. 

Key characteristics about activation functions:

Role in neural networks: They transform the weighted sum of inputs received by a neuron into an output value that is passed on to the next layer.
Non-linearity: The primary purpose of activation functions is to introduce non-linearity, enabling neural networks to learn complex patterns in data.
Decision making: By applying a mathematical operation to the input, the activation function decides whether the input is significant enough to "activate" the neuron.

Examples of activation functions:

• Sigmoid: Outputs a value between 0 and 1, often used in older neural networks
• ReLU (Rectified Linear Unit): Outputs the input if positive, otherwise 0, widely used in modern deep learning due to its computational efficiency
• Tanh (Hyperbolic tangent): Outputs a value between -1 and 1
• Softmax: Used for multi-class classification problems, outputs a probability distribution over different classes

Activation functions are an integral building block of neural networks that enable them to learn complex patterns in data. They transform the input signal of a node in a neural network into an output signal that is then passed on to the next layer.