Phase 07: Transformer 深入

本阶段包含 16 课时。

原始课程来源：AI Engineering from Scratch (MIT License)

---

• Why Transformers — The Problems with RNNs
• Self-Attention from Scratch
• Multi-Head Attention
• Positional Encoding — Sinusoidal, RoPE, ALiBi
• The Full Transformer — Encoder + Decoder
• BERT — Masked Language Modeling
• GPT — Causal Language Modeling
• T5, BART — Encoder-Decoder Models
• Vision Transformers (ViT)
• Audio Transformers — Whisper Architecture
• Mixture of Experts (MoE)
• KV Cache, Flash Attention & Inference Optimization
• Scaling Laws
• Build a Transformer from Scratch — The Capstone
• Attention Variants — Sliding Window, Sparse, Differential
• Speculative Decoding — Draft, Verify, Repeat