Week 9 - Transformer Architecture & Implementation

Week: 2025-11-03 to 2025-11-07
Status: “Done”

Week 9 Overview

This week explores the Transformer architecture, a revolutionary model that replaced RNNs in NLP. We’ll understand why transformers are needed, how they work internally, and implement them from scratch. From attention mechanisms to the full encoder-decoder design, this week bridges theory and practical implementation.

Key Topics

RNN Limitations & Transformer Introduction

  • Sequential processing bottlenecks in RNNs
  • Vanishing gradient problems
  • Information bottleneck with long sequences
  • Why attention is all you need

Transformer Architecture

  • Encoder-decoder structure
  • Multi-head attention layers
  • Positional encoding
  • Residual connections & layer normalization
  • Feed-forward networks

Attention Mechanisms

  • Scale dot-product attention (core mechanism)
  • Self-attention (same sentence)
  • Masked attention (decoder)
  • Encoder-decoder attention
  • Multi-head attention for parallel computation

Transformer Decoder & GPT2

  • Positional embeddings
  • Decoder block implementation
  • Feed-forward layer design
  • Output probability calculation

Applications & Models

  • GPT-2 (Generative Pre-trained Transformer)
  • BERT (Bidirectional Encoder Representations)
  • T5 (Text-to-Text Transfer Transformer)
  • Applications: Translation, Classification, QA, Summarization, Sentiment Analysis

Learning Objectives

  • ✅ Understand RNN limitations and why transformers solve them
  • ✅ Grasp the complete transformer architecture
  • ✅ Implement attention mechanisms from scratch
  • ✅ Build a transformer decoder (GPT2-style)
  • ✅ Recognize transformer applications and state-of-the-art models

Daily Breakdown

Day Focus Topics
41 RNN Problems Sequential processing, Vanishing gradients, Information bottleneck
42 Architecture Overview Encoder-decoder, Multi-head attention, Positional encoding
43 Attention Core Scale dot-product attention formula, Matrix operations, GPU efficiency
44 Attention Types Self-attention, Masked attention, Encoder-decoder attention
45 Decoder Implementation GPT2 architecture, Building blocks, Code walkthrough

Prerequisites

  • Deep understanding of RNNs, LSTMs, and attention from Week 8
  • Comfortable with matrix operations and linear algebra
  • PyTorch or TensorFlow knowledge helpful

Next Steps

  • Study the paper “Attention is All You Need” (Vaswani et al., 2017)
  • Implement transformer components incrementally
  • Experiment with pre-trained models (BERT, GPT-2, T5)