# Optimizer Demonstration
📝 **This chapter is under construction.**
This case study demonstrates SGD and Adam optimizers for gradient-based
optimization, following EXTREME TDD principles.
**Topics covered:**
- Stochastic Gradient Descent (SGD)
- Momentum optimization
- Adam optimizer (adaptive learning rates)
- Loss function comparison (MSE, MAE, Huber)
**See also:**
- [What is EXTREME TDD?](../methodology/what-is-extreme-tdd.md)
- [Performance Optimization](../refactor-phase/performance-optimization.md)