The Math Behind Transformers

Deep Dive into the Transformer Architecture, the key element of LLMs. Let’s explore its math, and build it from scratch in Python.

Image generated by DALL-E

Transformers have revolutionized the landscape of machine learning, fundamentally changing how we handle sequential data. Since their debut in 2017, these models have become the gold standard in natural language processing and image recognition. They are the driving force behind one of the most impressive innovations in AI: large language models (LLMs). In this article, you’ll delve into the math behind transformers, master their architecture, and understand how they work. Stick around until the end, as we’ll build a transformer from scratch using Python. Let’s dive in and explore one of the most powerful models in modern machine learning.

Index

· 1. Introduction
∘ 1.1 Background
∘ 1.2 What is a Transformer?
· 2. Architecture of Transformers
∘ 2.1 Overall Structure
∘ 2.2 Encoder
∘ 2.3 Decoder
∘ 2.4 Attention Mechanism
· 3. The Math Behind Transformers
∘ 3.1 Attention Mechanism
∘ 3.2 Multi-Head Attention
∘ 3.3 Position-Wise Feed-Forward Network
∘ 3.4 Layer Normalization and Residual Connections
∘ 3.5 Positional Encoding
∘ 3.6 Encoder
∘ 3.7 Decoder
· 4. Implementing Transformers from Scratch
∘ 4.1. Multi-Head…