ReLU is the most popular activation function in deep learning because it’s super simple and makes AI learn much faster.
What it does (main point):
- Positive input? → Passes it exactly as is to the next layer
- Negative or zero input? → Outputs 0 (blocks it, nothing passes)
ReLU(x) = max(0, x)
Why this makes AI learn fast:
- No "squashing" like old functions (Sigmoid/Tanh) → gradients don’t vanish
- Very fast to compute (just check if > 0)
- Many neurons turn off (output 0) → less work, faster training
Quick comparison:
- Sigmoid → slow, vanishing gradient
- Tanh → better but still slow
- ReLU → fast, no vanishing gradient (for positive values)
That's why almost every modern neural network (CNNs, Transformers, etc.) uses ReLU by default.
One small issue: Sometimes neurons "die" (always output 0 and stop learning).
Solution: Use Leaky ReLU or similar if needed.
Main thing in one line:
ReLU lets only positive signals pass through fully and blocks negative ones → this simple rule makes deep learning train fast and powerful.
Top comments (0)