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CodeSignal

Deconstructing the Transformer Architecture

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Overview

You'll systematically build the Transformer architecture from scratch, creating Multi-Head Attention, feed-forward networks, positional encodings, and complete encoder/decoder layers as reusable PyTorch modules.

Syllabus

  • Unit 1: Multi-Head Attention Mechanism
    • Building Parallel Attention
    • Building Strong Neural Foundations
    • Building Selective Attention Mechanisms
    • Tensor Surgery for Attention Heads
    • Bringing Attention Heads Back Together
  • Unit 2: Feed-Forward Networks and AddNorm
    • Building Feed Forward Network Components
    • Initialize Network Weights
    • Building Transformer Stability Components
    • Building Your First Transformer Block
  • Unit 3: Positional Encodings Explained
    • Building Mathematical Position Awareness
    • Scaling and Combining Embeddings
    • Debugging Faulty Encoding Logic
    • Runtime Error Detective Work
  • Unit 4: Building the Transformer Encoder
    • Building the Encoder Foundation
    • Bringing the Encoder to Life
    • Assembling the Full Transformer Stack
    • Building Your Complete Encoder Pipeline
  • Unit 5: Constructing the Transformer Decoder
    • Travel Through Transformers!
    • Building Your First Decoder Layer
    • Complete the Missing Connection
    • Assembling Full Decoder Layer
    • Building the Decoder Stack

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