📖 Transformers: The Architecture That Changed NLP

• 🧠 Why Transformers?
  • 🔄 Limits of RNNs and CNNs for Sequential Data
  • 🔗 Need for Long-Range Dependencies
  • ⏱️ Parallelism and Efficiency
• 🏗️ Core Building Blocks
  • 📦 Embeddings
  • 🎯 Positional Encoding
  • 🧮 Self-Attention Mechanism
  • 🧠 Multi-head Attention
  • 🧱 Feedforward Layers
  • 🔁 Layer Norm, Skip Connections
• 🔬 The Transformer Block
  • 🔁 Encoder Block (Structure + Flow)
  • 🔁 Decoder Block (Structure + Flow)
  • 🔄 Masking in Attention
  • 📶 Stack of N Layers
• 🔢 Attention Mechanism in Depth
  • 🧠 Attention as Weighted Lookup
  • 📏 Query, Key, Value Vectors
  • 📊 Dot-Product Attention Calculation
  • ⚙️ Softmax + Scaling
• 🧰 Training a Transformer Model
  • 📊 Example: Sequence Classification or Translation
  • 💡 Tokenization (WordPiece/BPE) Basics
  • 🧮 Input-Output Pipeline
• 📚 Transformers Beyond Text
  • 🧠 Use in Vision (ViT)
  • 🧪 Time Series and Tabular Data
  • 🧬 Multimodal Transformers
• 🧭 From Transformers to LLMs
  • 🌍 Evolution: Transformer → GPT/BERT → LLMs
  • 📈 Scaling Laws (Depth, Width, Data)
  • 🔍 Pretraining Objectives: Causal vs. Masked
• 🔚 Closing Notes
  • ⚠️ Conceptual Pitfalls
  • 🔍 Visual Explainers and Demos to Explore
  • 🚀 Next Up: Large Language Models (04)

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🧠 Why Transformers?

🔄 Limits of RNNs and CNNs for Sequential Data

🔗 Need for Long-Range Dependencies

⏱️ Parallelism and Efficiency

Back to the top

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🏗️ Core Building Blocks

📦 Embeddings

🎯 Positional Encoding

🧮 Self-Attention Mechanism

🧠 Multi-head Attention

🧱 Feedforward Layers

🔁 Layer Norm, Skip Connections

Back to the top

---

🔬 The Transformer Block

🔁 Encoder Block (Structure + Flow)

🔁 Decoder Block (Structure + Flow)

🔄 Masking in Attention

📶 Stack of N Layers

Back to the top

---

🔢 Attention Mechanism in Depth

🧠 Attention as Weighted Lookup

📏 Query, Key, Value Vectors

📊 Dot-Product Attention Calculation

⚙️ Softmax + Scaling

Back to the top

---

🧰 Training a Transformer Model

📊 Example: Sequence Classification or Translation

💡 Tokenization (WordPiece/BPE) Basics

🧮 Input-Output Pipeline

Back to the top

---

📚 Transformers Beyond Text

🧠 Use in Vision (ViT)

🧪 Time Series and Tabular Data

🧬 Multimodal Transformers

Back to the top

---

🧭 From Transformers to LLMs

🌍 Evolution: Transformer → GPT/BERT → LLMs

📈 Scaling Laws (Depth, Width, Data)

🔍 Pretraining Objectives: Causal vs. Masked

Back to the top

---

🔚 Closing Notes

⚠️ Conceptual Pitfalls

🔍 Visual Explainers and Demos to Explore

🚀 Next Up: Large Language Models (04)

Back to the top

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