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?
• Performance Optimization