Activation functions give neural networks the power to learn non-linear patterns. This guide breaks down Sigmoid, Tanh, ReLU, and modern variants like Leaky ReLU, ELU, and SELU—explaining how they work, why they matter, and how they impact training performance.

Overfitting occurs when a neural network memorizes training data instead of learning real patterns. This guide explains how Dropout works, why it is effective, and how to tune it to build robust models.