What are activation functions?

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Activation functions are mathematical functions used in artificial neural networks to determine whether a neuron should be activated (fire) or not. They introduce non-linearity into the network, allowing it to learn complex patterns and relationships in data. Without activation functions, a neural network would behave like a simple linear model, limiting its learning capability.

Why are they important?

  • Enable the network to learn non-linear relationships.

  • Control the output range of neurons.

  • Help in gradient-based optimization by making backpropagation possible.

  • Decide how much of the input signal should pass to the next layer.

Common Types of Activation Functions:

  1. Sigmoid Function (Logistic):

    • Output range: (0, 1).

    • Good for probabilities but suffers from vanishing gradients.

  2. Tanh (Hyperbolic Tangent):

    • Output range: (-1, 1).

    • Zero-centered but still faces vanishing gradient issues.

  3. ReLU (Rectified Linear Unit):

    • Output: 0 if input < 0, otherwise input.

    • Most widely used due to simplicity and efficiency.

    • Can suffer from the “dying ReLU” problem (neurons stuck at zero).

  4. Leaky ReLU:

    • Variant of ReLU that allows a small negative output instead of 0.

    • Solves the dying ReLU problem.

  5. Softmax:

    • Converts outputs into probability distributions (values between 0 and 1 that sum to 1).

    • Commonly used in the output layer for classification problems.

✅ In short: Activation functions decide how signals flow through a neural network, making it capable of learning complex, real-world patterns.

Read More:

What is a neural network?

What are stop words in NLP?

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