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Activation Function

An activation function is a mathematical function used in artificial neural networks to determine the output of a neuron. It takes the weighted sum of the input signals and applies a transformation, introducing non-linearity into the model, which is crucial for learning complex patterns.

Use Cases

Binary Classification

Used in neural networks to decide whether an email is spam or not based on input features.

Image Recognition

Helps neural networks to identify objects in images by processing visual data through multiple layers.

Importance

Enables Learning

Activation functions allow neural networks to learn and represent complex patterns and relationships in data.

Non-Linearity

Introduces non-linear properties, enabling neural networks to solve complex tasks beyond linear functions.

Gradient Flow

Influences the flow of gradients during backpropagation, affecting the learning process and model performance.

Analogies

Think of an activation function like a gatekeeper in a theme park. Only when certain conditions are met (like having a valid ticket), the gatekeeper allows people (signals) to enter the park (neuron activation). Different gatekeepers (activation functions) have different criteria (functions) for allowing entry.

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