Introduction to LLM

This page provides an easy-to-understand guide on LLMs (Large Language Models) from basics to applications for AI enthusiasts.


Total of 6 articles available. | Currently on page 1 of 1.

Chapter 8 — How Models Learn

Eighth post of the LLM Primer II walkthrough. Why over-parameterized models generalize at all, the implicit bias of gradient-based optimization, the empirical scaling laws that forecast capability before training, and the open mathematical questions that still surround LLM theory.

2026-03-10

Chapter 7 — Efficiency and Transformer Variants

Seventh post of the LLM Primer II walkthrough. The computational complexity of attention, the GPU memory and throughput math that constrains real systems, FlashAttention derived from first principles, and the family of clever variants — multi-query, gated, low-rank — that keep big models running.

2026-03-09

LLM Primer II — Language Models Through Mathematics: Series Introduction & Index

Kicking off the chapter-by-chapter walkthrough of Book II in the LLM Primer series — Language Models Through Mathematics. How the book is organized, what each chapter delivers, and the schedule for the fourteen posts that follow, March 3 through March 16.

2026-03-02

Chapter 3 — Neural Networks for Language: From RNNs to Self-Attention

Chapter 3 of the LLM Primer I series. Why feedforward networks couldn't handle language, how RNNs hit a wall, and what attention changed. A clean conceptual progression through the three neural-network shapes that defined modern NLP — without the math anxiety.

2026-02-20

Understanding LLMs – A Mathematical Approach to the Engine Behind AI

A preview from Chapter 7.4: Discover why large language models inherit bias, the real-world risks, strategies for mitigation, and the growing role of AI governance.

2025-09-01

3.2 LLM Training Steps: Forward Propagation, Backward Propagation, and Optimization

Explore the key steps in training Large Language Models (LLMs), including initialization, forward propagation, loss calculation, backward propagation, and hyperparameter tuning. Learn how these processes help optimize model performance.

2024-09-13