Rami

Demystify AI and Machine Learning


Continuous Distribution & PDF

Understand continuous probability distributions and PDF. Learn how histograms become density functions with intuitive manufacturing examples.

11 min read   ·   2025

Normal Distribution

Complete guide to Normal (Gaussian) distribution. Learn standard normal, mean, variance, sigma, and why it matters for machine learning algorithms.

9 min read   ·   2025

Random Variables

Understand random variables with intuitive examples. Learn discrete vs continuous random variables, PMF, PDF, and probability distributions for machine learning.

7 min read   ·   2025

Understanding Derivatives

Learn derivatives intuitively with visual examples. Understand slope, rate of change, gradient, and why derivatives matter for gradient descent in ML.

11 min read   ·   2025

Self Attention

Master self-attention mechanism in transformers. Learn Queries, Keys, Values, scaled dot-product attention with intuitive examples and mathematical derivations.

12 min read   ·   2025

Transformer Model Architecture

Complete guide to Transformer architecture. Learn encoder, decoder, multi-head attention, positional encoding, and masked attention with detailed explanations.

12 min read   ·   2024

Birth of Attention Mechanism

Learn how attention mechanism works in seq2seq encoder-decoder models. Understand context vectors, attention weights, and why attention solves the long sequence problem.

12 min read   ·   2024