Activation Functions in Neural Networks

Introduction

• Presenter: Jay Patel
• Overview of activation functions in neural networks, their necessity, types, and usage.
• Encouragement to subscribe for more tutorials on machine learning.

Why Do We Need Activation Functions?

• Activation functions introduce non-linearity to the neural network model.
• Forward propagation equations without activation functions lead to linear outputs:
  • Without activation, multiple layers function similarly to a single layer.
  • Non-linear activation functions allow for learning complex relationships in real-world data.

Types of Activation Functions

1. Sigmoid Function

• S-shaped curve, outputs values between 0 and 1.
• Commonly used in the output layer for binary classification.
  • Example: Classifying apples vs. oranges.
• Drawbacks in hidden layers:
  • Limited derivative (max 0.25), leading to slower training.

2. Tanh (Hyperbolic Tangent) Function

• Outputs values between -1 and 1.
• Advantages over sigmoid:
  • Higher derivative (up to 1), faster convergence during training.
  • Data output is centered around zero, normalizing input for next layers.

3. ReLU (Rectified Linear Unit) Function

• Outputs 0 for x ≤ 0 and x for x > 0.
• Overcomes vanishing gradient problem.
• Fast learning due to constant derivative (value = 1 for x > 0).
• Piecewise linear function, providing benefits of both linearity and non-linearity.
• Variants:
  • Leaky ReLU: Outputs a small negative slope (e.g., 0.01 * x) for x < 0.
  • ELU (Exponential Linear Unit): Uses an exponential component for x < 0.

4. Softmax Function

• Used for multi-class classification problems.
• Converts raw output values into probabilities by exponentiating and normalizing.
• Ensures that the sum of output probabilities equals 1.

5. Linear Activation Function

• Used in the output neuron for linear regression problems.
• No activation function applied to maintain linearity in outputs.

Summary of Activation Function Usage

• Binary Classification: Use sigmoid function at output layer.
• Hidden Layers: Prefer tanh or ReLU functions to deal with vanishing gradient issues.
• Multi-class Classification: Use softmax function for output layer.
• Linear Regression: No activation function in output neuron.
• Other activation functions exist, but these are the most commonly used.
• Choice between tanh and ReLU can depend on the specific application and data.

Conclusion

• Encouragement to explore different activation functions based on application needs.
• Reminder to subscribe for future content, including upcoming videos on backpropagation.