NLP Foundations Consolidation #
This page is a structured learning template. Replace the comments with clear explanations, examples, formulas, diagrams, and practical insights while keeping the Hugo shortcodes intact.
Learning Objectives #
- Connect the progression from linguistic analysis to statistical and neural modelling.
- Connect the key definitions, models, algorithms, and evaluation ideas from the foundational chapters.
- Practise concise explanations, comparisons, and short calculations without relying on copied text.
- Identify weak areas and create a focused learning plan.
Chapter Map #
| Section | Topic | Status |
|---|---|---|
| 1 | Natural Language Understanding and Generation | ☐ |
| 2 | Vector Semantics and Embedding | ☐ |
| 3 | N-gram Language Modelling | ☐ |
| 4 | Neural Language Modelling | ☐ |
| 5 | LLMs and Prompt Engineering | ☐ |
| 6 | Part-of-Speech Tagging | ☐ |
| 7 | Statistical, ML and Neural POS Tagging | ☐ |
Big Picture #
1. Natural Language Understanding and Generation ☆ #
Definition #
Intuition #
Key Concepts #
Formula or Model #
Worked Example #
Why It Matters in NLP #
Key Points to Remember #
2. Vector Semantics and Embedding ☆ #
Definition #
Intuition #
Key Concepts #
Formula or Model #
Worked Example #
Why It Matters in NLP #
Key Points to Remember #
3. N-gram Language Modelling ☆ #
Definition #
Intuition #
Key Concepts #
Formula or Model #
Worked Example #
Why It Matters in NLP #
Key Points to Remember #
4. Neural Language Modelling ☆ #
Definition #
Intuition #
Key Concepts #
Formula or Model #
Worked Example #
Why It Matters in NLP #
Key Points to Remember #
5. LLMs and Prompt Engineering ☆ #
Definition #
Intuition #
Key Concepts #
Formula or Model #
Worked Example #
Why It Matters in NLP #
Key Points to Remember #
6. Part-of-Speech Tagging ☆ #
Definition #
Intuition #
Key Concepts #
Formula or Model #
Worked Example #
Why It Matters in NLP #
Key Points to Remember #
7. Statistical, ML and Neural POS Tagging ☆ #
Definition #
Intuition #
Key Concepts #
Formula or Model #
Worked Example #
Why It Matters in NLP #
Key Points to Remember #
Practical Exploration #
Complete a mixed consolidation set containing definitions, comparisons, calculations, and short algorithm traces.
# Add a minimal, well-commented Python example here.
Comparison Table #
| Concept or Model | Main Idea | Strength | Limitation | Typical Use |
|---|---|---|---|---|
Common Mistakes #
Practice Questions #
Key Takeaways #
Understanding Checklist #
- I can explain Natural Language Understanding and Generation without referring to notes.
- I can explain Vector Semantics and Embedding without referring to notes.
- I can explain N-gram Language Modelling without referring to notes.
- I can explain Neural Language Modelling without referring to notes.
- I can explain LLMs and Prompt Engineering without referring to notes.
- I can explain Part-of-Speech Tagging without referring to notes.
- I can explain Statistical, ML and Neural POS Tagging without referring to notes.