Why does a neural network need an activation function?

How It Works:

Activation functions transform a neuron?s weighted sum into a nonlinear output, enabling networks to approximate complex patterns instead of just linear relationships.

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Key Benefits:

• Expressivity: Learns intricate mappings from inputs to outputs.
• Feature hierarchy: Builds richer representations layer by layer.
• Versatility: Different functions suit different tasks.

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Real-World Use Cases:

• Image classification: ReLU in convolutional nets for object detection.
• Binary classification: Sigmoid at output layers for probability scores.