Week 3: Building a Real-World Housing Price Predictor
Course Generated Slides: AutoML | Bayesian Methods | Boosting & Bagging | Cross Validation | Decision Trees | Regularization
1. Problem Setup & Data Exploration
- California Housing Dataset introduction
- Understanding the features
- Visualization of relationships
- Statistical analysis
- Data preprocessing pipeline
- Feature scaling
- Handling missing values
- Train/validation/test splits
2. Model Development
- Building the network architecture
- Input layer design
- Hidden layer configuration
- Output layer for regression
- Implementing in PyTorch
- Dataset class creation
- DataLoader setup
- Model class definition
- Training loop implementation
- Forward pass
- Backward pass
- Optimization step
3. Model Evaluation & Improvement
- Performance metrics
- MSE vs MAE
- R-squared evaluation
- Iterative improvement
- Learning rate tuning
- Architecture adjustments
- Regularization application
- Model validation
- Overfitting detection
- Cross-validation implementation
- Error analysis
Required Reading
- Deep Learning Book (Goodfellow et al.) - Chapter 11: Practical Methodology
- California Housing Dataset Documentation
Learning Objectives
- Build a complete deep learning pipeline from scratch
- Apply Week 1-2 concepts to a real problem
- Implement proper validation techniques
- Debug and improve model performance