AI Practitioner

Activation functions are a  core component  of deep learning models, especially neural networks. They determine whether a neuron should be activated or not, introducing  non-linearity  into the model. Without activation functions, a neural network would behave like a  linear regression model , no matter how many layers it had. Role of Activation Functions Non-linearity : Real-world data is non-linear. Activation functions allow the model to learn complex patterns. Enabling deep learning : Deep networks rely on stacking layers. Without non-linearity, stacking layers is meaningless. Gradient flow : The function affects how gradients propagate during backpropagation, impacting training speed and performance. Evolution of Activation Functions Let’s walk through a timeline of activation functions and why newer ones replaced earlier ones. 1.  Sigmoid Function Formula : $\sigma(x)=\frac{1}{1+e^{-x}}(x)$ ​ Range : (0, 1) Problems : Vanishing gradients for larg...