PART V — Transformer Architecture (The Core)

 

Chapter 8: Why Transformers Replaced RNNs

Goal: Explain the breakthrough

Topics Covered:

• Limitations of RNNs and LSTMs

• Parallelization advantages

• Long-range dependencies

• Transformer design philosophy

📌 Medium Post 8: Why Transformers Changed Everything

---

Chapter 9: Self-Attention Mechanism Deep Dive

Goal: Core technical understanding

Topics Covered:

• Query, Key, Value explained

• Attention scores

• Softmax weighting

• Intuition behind attention

• Visualization of attention flow

📌 Medium Post 9: Self-Attention Explained Intuitively

---

Chapter 10: Multi-Head Attention

Goal: Advanced attention understanding

Topics Covered:

• Why multiple heads are needed

• How heads specialize

• Concatenation & projection

• Computational cost

📌 Medium Post 10: Why LLMs Use Multi-Head Attention

---

Chapter 11: Feed-Forward Networks in Transformers

Goal: Complete the transformer block

Topics Covered:

• Position-wise feed-forward layers

• Activation functions (ReLU, GELU)

• Why FFNs are needed after attention

📌 Medium Post 11: The Hidden Power of Feed-Forward Layers

---

Chapter 12: Positional Encoding

Goal: Order & sequence understanding

Topics Covered:

• Why transformers need position info

• Sinusoidal encodings

• Learned positional embeddings

• Rotary embeddings (RoPE)

• Impact on long-context models

📌 Medium Post 12: How Transformers Understand Order