Transformers

Master the architecture that powers modern AI, from attention mechanisms to GPT, BERT, and beyond

Part 1

Prerequisites

Essential background for understanding transformers

Neural Network Foundations

Parameters, gradients, and GPU computation

Part 2

Foundations

The building blocks of transformer architecture

The Sequence Problem

Why sequences are hard for computers

From Words to Numbers

Tokenization and embeddings

The Attention Intuition

What attention really means

Positional Encoding

How transformers know word order

Part 3

The Attention Mechanism

Understanding the core innovation

Scaled Dot-Product Attention

The core operation

Multi-Head Attention

Learning multiple relationships

Self-Attention vs Cross-Attention

Different attention patterns

Attention Patterns in Practice

What models actually learn

Part 4

The Full Architecture

Assembling the complete transformer

The Transformer Block

Putting pieces together

The Encoder Stack

Understanding context bidirectionally

The Decoder Stack

Generating output autoregressively

Part 5

Transformer Variants

Different architectures for different tasks

BERT Deep Dive

Bidirectional understanding

GPT Deep Dive

Autoregressive generation

Vision Transformers

Transformers see images

Whisper and Audio

Transformers hear sounds

Multimodal Transformers

Combining vision, language, and beyond

Part 6

Real-World Systems

From theory to production

Training Transformers

From random to intelligent

Scaling Laws and Emergence

Why bigger is different

How LLMs Work

Inside a modern language model

The Inference Pipeline

Making generation fast

Fine-Tuning and Adaptation

Customizing models

Alignment and Safety

Making models helpful and harmless

RAG and Vector Search

Grounding in external knowledge

Prompt Engineering

Techniques to get the best outputs

Tool Calling and Agents

Interacting with the world

What Comes Next

The frontier of research