Batch Normalization is a powerful technique that stabilizes and accelerates the training of deep neural networks by normalizing layer activations. This article explains the intuition behind Batch Normalization, internal covariate shift, the step-by-step algorithm, and why BN improves convergence, gradient flow, and overall training stability.

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.

The Perceptron is one of the simplest yet most important algorithms in supervised learning. Acting as the foundation for modern neural networks, it uses inputs, weights, and an activation function to make binary predictions. In this guide, we explore how the Perceptron learns, interprets weights, and forms decision boundaries — along with its biggest limitation: linear separability.