Activation Functions in Deep Learning
Activation functions control how neurons respond inside a neural network. They add non linear behavior. This helps the model learn complex patterns.
Why Activation Functions Are Important
- Help networks learn non linear relations
- Guide gradient flow
- Control output ranges
- Improve training stability
Common Activation Functions
1. Sigmoid
Output stays between zero and one. Useful for binary classification.
Pros
- Clear output range
- Good for probability outputs
Cons
- Slow gradients
- Vanishing gradients in deep networks
2. Tanh
Output stays between minus one and one. Stronger signal range than sigmoid.
Pros
- Zero centered output
- Better gradients than sigmoid
Cons
- Still suffers from vanishing gradients
3. ReLU
Returns zero for negative inputs and the input value for positive ones. ReLU is widely used in deep networks.
Pros
- Fast computation
- Strong gradient flow
- Supports deep architectures
Cons
- Dead neurons if weights push many values below zero
4. Leaky ReLU
Solves ReLU dead neuron issue by giving a small slope for negative inputs.
Pros
- Less dead neurons
- Stable gradients
Cons
- Extra hyperparameter for slope
5. Softmax
Turns outputs into probabilities that sum to one. Used in multi class classification.
Pros
- Clear probability distribution
- Strong for classification
Cons
- Sensitive to large values
6. GELU
A smooth activation used in transformer models. Offers stable and flexible behavior.
Pros
- Better performance in modern networks
- Smoother than ReLU
Cons
- More compute than ReLU
How To Choose an Activation Function
- Use ReLU or GELU for most deep models
- Use sigmoid for binary output
- Use softmax for multi class output
- Use tanh in networks that need centered values
Activation Functions in Moroccan Darija
Activation functions kaykhdmo bach yzido non linearity f neural network. Hadi katkhlli model y9der y3rf patterns m3a9din.
Sigmoid
Output bin zero w one. Mzyan f binary classification.
Tanh
Output bin minus one w one. Zero centered.
ReLU
Zero ila input negative. Input ila positive. Sahl w rapide.
Leaky ReLU
Kayssir 3la problem dial dead neurons b slope sghir f negative.
Softmax
Kaydiro probabilities f classification multi class.
Conclusion
Activation functions guide how networks learn. They shape gradients, outputs, and training stability. Choosing the right one strengthens model performance.