· Deep Learning · 2 min read
📋 Prerequisites
- Basic understanding of neural networks
🎯 What You'll Learn
- Understand what activation functions are
- Learn why activation functions are important
- Explore commonly used activation functions
- Gain confidence in using activation functions in your models
Introduction
Activation functions are mathematical functions applied to the output of neurons in neural networks to introduce non-linearity, enabling the network to learn complex patterns in data.
Why Are Activation Functions Important?
✅ Without activation functions, neural networks would be equivalent to linear models regardless of depth.
✅ They allow models to capture complex, non-linear relationships in data.
✅ They enable neural networks to solve classification, detection, and prediction tasks effectively.
Common Activation Functions
1️⃣ ReLU (Rectified Linear Unit)
[ f(x) = \max(0, x) ]
✅ Simple and widely used in hidden layers.
✅ Helps avoid vanishing gradients.
2️⃣ Sigmoid
[ \sigma(x) = \frac{1}{1 + e^{-x}} ]
✅ Squashes values between 0 and 1, useful for binary classification outputs.
✅ Can suffer from vanishing gradients in deep networks.
3️⃣ Tanh (Hyperbolic Tangent)
[ \tanh(x) = \frac{e^x - e^{-x}}{e^x + e^{-x}} ]
✅ Squashes values between -1 and 1, zero-centered, useful for hidden layers.
4️⃣ Leaky ReLU
Allows a small, non-zero gradient when the unit is inactive:
[ f(x) = \begin{cases} x & \text{if } x > 0 \ \alpha x & \text{if } x \leq 0 \end{cases} ]
✅ Helps mitigate the “dying ReLU” problem.
5️⃣ Softmax
Used in the output layer for multi-class classification, converts outputs into probabilities that sum to 1.
Example: Using Activation Functions in TensorFlow
import tensorflow as tf
model = tf.keras.Sequential([
tf.keras.layers.Dense(128, activation='relu', input_shape=(784,)),
tf.keras.layers.Dense(10, activation='softmax') # For multi-class output
])Choosing the Right Activation Function
✅ ReLU for hidden layers in most deep learning models.
✅ Sigmoid for binary classification outputs.
✅ Softmax for multi-class classification outputs.
Conclusion
Activation functions are essential for enabling deep learning models to learn complex, non-linear patterns in data.
What’s Next?
✅ Experiment with different activation functions in your projects.
✅ Continue your deep learning journey on superml.org.
Join the SuperML Community to discuss and explore how activation functions affect your models.
Happy Learning! ✨