102-4 hours

Capstone Project

Train Your Own Model

Module 10: Capstone Project - Train Your Own Model

Apply everything you have learned by building a complete character-level language model

Congratulations on reaching the capstone! This module brings together everything from the course: RNN architecture, training dynamics, loss functions, sampling strategies, and evaluation metrics. You will build a complete, working language model from scratch.

Choose your difficulty level based on your experience and time commitment. Each level offers a unique challenge while reinforcing the core concepts.

Why Build Your Own Model?

For Your Manager

The capstone project transforms conceptual understanding into hands-on experience. By training your own model, you will gain intuition for hyperparameter tuning, data quality, and the practical challenges that arise in real AI projects.

For Your Manager

🎯Choose Your Challenge

Project Difficulty Levels

Select based on your experience and available time

🌱

Beginner

Dataset: Song Lyrics (~500KB)

Goal: Generate coherent lyrics with rhyme patterns

Success Metrics:

Loss < 1.5
Recognizable words
Basic structure

🌿

Intermediate

Dataset: Code Generator (~5MB)

Goal: 80%+ syntactically parseable code

Success Metrics:

Loss < 1.2
80% parseable
Correct indentation

🌳

Advanced

Dataset: Multi-Author Style (~10MB)

Goal: Human evaluators identify correct author 70%+

Success Metrics:

Style transfer accuracy
Human evaluation
Perplexity < 50

Recommendation

Start with Beginner even if you have experience. The song lyrics project trains quickly and provides immediate feedback.

🗺️Your Journey

5 Project Milestones

Complete each milestone to earn badges and build your model

Data Preparation

📊 Data Wrangler

Collect, clean, and preprocess your training corpus

☐ Choose and download your dataset
☐ Clean text (remove special characters)
☐ Create character/token vocabulary
☐ Split into train/validation/test (80/10/10)

Model Architecture

🏗️ Architect

Design and implement your RNN architecture

☐ Choose RNN variant (vanilla, LSTM, GRU)
☐ Define embedding layer dimensions
☐ Set hidden state size (128-512 typical)
☐ Implement forward pass

Training Loop

🏋️ Trainer

Implement robust training with proper optimization

☐ Implement cross-entropy loss
☐ Set up optimizer (Adam, lr=0.001)
☐ Add gradient clipping (max_norm=5)
☐ Implement learning rate scheduling

Evaluation

📈 Evaluator

Measure model quality with multiple metrics

☐ Calculate validation perplexity
☐ Generate samples at different temperatures
☐ Compare against baseline (random, n-gram)
☐ Analyze failure cases

Presentation

🎤 Presenter

Document and share your results

☐ Write project summary
☐ Create visualizations (loss curves, samples)
☐ Document hyperparameter choices
☐ Reflect on what worked and what did not

🏆Gamification

Achievement Badges

Earn badges as you progress through your project

🎯

First Loss

Complete your first training epoch

📉

Loss Crusher

Achieve loss below 1.5

🧠

Perplexity Pro

Achieve perplexity below 50

✍️

Sample Master

Generate 100 coherent samples

🌡️

Temperature Explorer

Experiment with 5 different temperatures

🎓

Course Graduate

Complete the entire capstone project

📊Success Criteria

Evaluation Metrics

Perplexity

Lower is better. Represents how many characters the model is confused between. A good character-level model achieves PPL of 1.5-3.

Sample Quality

• Coherence: Do samples make sense?
• Style: Does it match the training data?
• Diversity: Are samples varied?

Benchmark Targets by Level

Level	Perplexity	Loss	Custom Metric
Beginner	< 5.0	< 1.5	Recognizable words
Intermediate	< 3.0	< 1.2	80% parseable code
Advanced	< 2.0	< 0.8	70% style accuracy

💡Pro Tips

Tips for Success

Training

✓Start with a small model and scale up
✓Use gradient clipping (max_norm=5)
✓Monitor validation loss for overfitting
✓Save checkpoints every 10 epochs

Debugging

✓Overfit on a tiny dataset first
✓Check that loss decreases initially
✓Generate samples every epoch to track progress
✓If loss explodes, reduce learning rate

Ready to Begin!

You now have everything you need to build your own character-level language model. Remember: the goal is not perfection, but understanding.

Your Next Steps

Choose your difficulty level
Collect your dataset
Work through the 5 milestones
Earn your badges
Share your results!

Congratulations on completing the RNN course! Whether you build a lyrics generator, code writer, or style mimic, you are now part of the lineage of researchers who discovered the unreasonable effectiveness of recurrent neural networks.