Transformer Models Explained | How ChatGPT Actually Works

Описание: In 2017, eight researchers at Google published "Attention is All You Need" - the paper that revolutionized artificial intelligence. Every modern AI you use - ChatGPT, GPT-4, DALL-E, Claude, Gemini, Midjourney - is built on ONE architecture from that paper: The Transformer.

This video explains EXACTLY how Transformers work, from the core mathematics to the complete architecture. No hand-waving. No "it just learns patterns." The actual mechanism that powers modern AI.

🎯 WHAT YOU'LL LEARN:

00:00 - Introduction: The Paper That Changed Everything
00:45 - The Problem: Why RNNs Failed
01:45 - Self-Attention Mechanism (CORE CONCEPT)
• Query, Key, Value vectors
• Attention score calculation
• Softmax normalization
• The formula: Attention(Q,K,V) = softmax(QK^T/√d_k)V

03:15 - Multi-Head Attention
• Why use multiple attention heads?
• Different heads learn different patterns
• GPT-3: 96 heads × 96 layers = 9,216 attention mechanisms

04:15 - Positional Encoding
• The word order problem
• Sinusoidal position embeddings
• Why sin/cos functions work

05:00 - Complete Architecture
• Encoder stack (BERT uses this)
• Decoder stack (GPT uses this)
• Residual connections
• Layer normalization
• How text generation works

06:30 - Training & Scaling Laws
• Next-token prediction objective
• GPT-2 (1.5B) → GPT-3 (175B) → GPT-4 (1.7T)
• Emergent abilities at scale
• Why scaling works so well

07:15 - Impact & Applications
• Language: GPT, BERT, Claude
• Vision: DALL-E, Stable Diffusion
• Protein: AlphaFold
• Music: Jukebox
• Video: Sora
• The universal architecture

📚 KEY CONCEPTS EXPLAINED:

✓ Self-Attention: How each word attends to every other word simultaneously
✓ Query/Key/Value: The three vectors that power attention
✓ Softmax: Converting attention scores to probabilities
✓ Multi-Head Attention: Parallel attention mechanisms learning different patterns
✓ Positional Encoding: Adding word order information using sinusoidal functions
✓ Encoder-Decoder: The two-part architecture (BERT vs GPT)
✓ Scaling Laws: Why bigger models are predictably better
✓ Emergent Abilities: Capabilities that appear only at certain scales

🔬 MATHEMATICAL FORMULAS COVERED:

• Self-Attention: Attention(Q,K,V) = softmax(QK^T/√d_k)V
• Multi-Head: MultiHead(Q,K,V) = Concat(head_1,...,head_h)W^O
• Positional Encoding: PE(pos,2i) = sin(pos/10000^(2i/d_model))
• Feed-Forward: FFN(x) = max(0, xW_1 + b_1)W_2 + b_2

🎓 WHO IS THIS FOR?

• Machine learning students learning transformer architecture
• Developers implementing transformers in PyTorch/TensorFlow
• Researchers understanding state-of-the-art NLP models
• AI enthusiasts curious about how ChatGPT actually works
• Anyone who wants to understand the foundation of modern AI

💡 WHY TRANSFORMERS WON:

1. PARALLELIZATION: Process all tokens simultaneously (RNNs can't do this)
2. LONG-RANGE DEPENDENCIES: No vanishing gradient problem
3. SCALABILITY: Performance improves predictably with more parameters
4. UNIVERSALITY: Same architecture works for text, images, audio, protein, video
5. EMERGENT ABILITIES: New capabilities appear automatically at scale

📖 RESOURCES & REFERENCES:

Original Paper: "Attention is All You Need" (Vaswani et al., 2017)
https://arxiv.org/abs/1706.03762

The Illustrated Transformer (Jay Alammar)
https://jalammar.github.io/illustrate...

Transformer Implementation Guide:
https://pytorch.org/tutorials/beginne...

GPT-3 Paper: "Language Models are Few-Shot Learners"
https://arxiv.org/abs/2005.14165

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⚡ Next video: Vision Transformers (ViT) - How Transformers Conquered Computer Vision