Neural Network and Neural Language Modelling #
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 #
- Describe the components of a feed-forward neural network for language modelling.
- Explain how embeddings, hidden layers, and output probabilities work together.
- Outline the training process, loss function, and parameter updates.
- Compare neural language models with count-based n-gram models.
Chapter Map #
| Section | Topic | Status |
|---|---|---|
| 1 | Feed-Forward Neural Networks | ☐ |
| 2 | Training Neural Networks for Language Models | ☐ |
| 3 | Neural Language Models | ☐ |
Big Picture #
1. Feed-Forward Neural Networks ☆ #
Definition #
Intuition #
Key Concepts #
Formula or Model #
Worked Example #
Why It Matters in NLP #
Key Points to Remember #
2. Training Neural Networks for Language Models ☆ #
Definition #
Intuition #
Key Concepts #
Formula or Model #
Worked Example #
Why It Matters in NLP #
Key Points to Remember #
3. Neural Language Models ☆ #
Definition #
Intuition #
Key Concepts #
Formula or Model #
Worked Example #
Why It Matters in NLP #
Key Points to Remember #
Practical Exploration #
Implement a small neural language model and inspect its training loss and predictions.
# 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 Feed-Forward Neural Networks without referring to notes.
- I can explain Training Neural Networks for Language Models without referring to notes.
- I can explain Neural Language Models without referring to notes.