Series · 🔒 Private
Foundations
Core mathematics and concepts behind modern ML — neural-net fundamentals, the softmax + cross-entropy stack, statistical inference. Each note stands alone; together they form the shared language of everything else in Data.
- 01Artificial Neural Networks — A Refresher 🔒Neurons, activations, the forward pass, loss, gradient descent and backpropagation, optimisers, and the families of neural networks — a bridge from classical ANN theory to the modern deep-learning era.7 min
- 02The Softmax Function 🔒How a vector of arbitrary scores becomes a probability distribution — the formula, why the exponential, temperature, numerical stability, the gradient, and why softmax + cross-entropy is the standard classifier head.4 min
- 03Boltzmann / Gibbs Distribution 🔒The physics-born distribution that quietly powers softmax, the temperature knob in LLM sampling, attention weights, and every energy-based model. Where it comes from, what it means, and where it shows up in modern deep learning.6 min
- 04Cross-Entropy 🔒The loss function behind virtually every classifier and every LLM pre-training run. Where it comes from (surprise & coding theory), why it punishes confident wrong predictions so brutally, and why it pairs so cleanly with softmax.8 min
- 05Probability Concepts 🔒Conditional probability, independence, the Law of Total Probability, and Bayes' Theorem — with the intuitive examples that make them stick, and why they sit at the heart of machine learning.4 min
- 06Statistics & Inference 🔒Sampling and practical sampling strategies, the Central Limit Theorem, hypothesis testing, and the z / t / chi-square tests — when to use each, what a p-value really means, and where it all applies.7 min