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🏗️ Transformer Architecture

Master the architecture that revolutionized modern AI

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Attention Mechanism Explorer

The Transformer Revolution

💡 "Attention Is All You Need"

In 2017, researchers at Google introduced the Transformer architecture in their groundbreaking paper "Attention Is All You Need." This model eliminated recurrence and convolutions entirely, relying solely on attention mechanisms to process sequences. This breakthrough enabled parallel processing and better long-range dependency modeling.

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Impact

Powers GPT, BERT, T5, and virtually all modern large language models. Transformed NLP, computer vision, and multi-modal AI.

❌ Before Transformers (RNNs/LSTMs)

  • •Sequential processing (slow)
  • •Vanishing gradients for long sequences
  • •Limited parallelization
  • •Hard to capture long-range dependencies

✅ With Transformers

  • •Parallel processing (fast training)
  • •Direct connections between all positions
  • •Highly parallelizable on GPUs
  • •Excellent long-range modeling
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Language Models

GPT series, BERT, T5, RoBERTa - all built on Transformer architecture

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Computer Vision

Vision Transformers (ViT), DINO, CLIP for image understanding

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Audio & More

Speech recognition, music generation, protein folding (AlphaFold)

🎯 Core Innovation

The Transformer's key insight: use attention to compute representations of sequences, allowing every position to attend to every other position simultaneously. This replaces sequential recurrence with parallel attention.

RNN: h_t = f(h_(t-1), x_t) ❌ Sequential
Transformer: h_i = Attention(Q, K, V) ✅ Parallel