What is ReLU activation?

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ReLU (Rectified Linear Unit) is one of the most widely used activation functions in deep learning. It introduces non-linearity into neural networks while keeping the computation simple and efficient.

Definition

ReLU outputs:

• 0 if the input is negative or zero

• the input itself if it is positive

Mathematically:
ReLU(x) = max(0, x)

Why It’s Popular

1. Computationally Efficient: Only requires a simple thresholding at zero.

2. Sparse Activation: Since negative inputs become zero, many neurons remain inactive, leading to efficient learning and reduced computation.

3. Mitigates Vanishing Gradient (partially): Unlike sigmoid or tanh, ReLU doesn’t saturate for large positive values, so gradients remain strong and help deep networks learn faster.

Limitations

• Dying ReLU Problem: If many inputs are negative, neurons can output zero consistently and stop learning (their gradients vanish).

• Unbounded Output: Positive values can grow indefinitely, which sometimes leads to unstable training if not managed.

Use Cases

• Hidden layers of deep neural networks (CNNs, RNNs, Transformers).

• Standard choice in most modern architectures due to speed and effectiveness.

👉 In short, ReLU is a simple yet powerful activation function that outputs positive values as-is and zero otherwise, enabling deep networks to learn complex patterns efficiently.

Read more:

What is backpropagation?

What are activation functions?

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