Overview

Three ML fundamentals theory chapters are currently stubs (1 line each):

• ml-fundamentals/gradient-descent.md
• ml-fundamentals/advanced-optimizers.md
• ml-fundamentals/feature-scaling.md

These chapters are referenced in SUMMARY.md but lack content. The codebase has working implementations (SGD, Adam, StandardScaler, MinMaxScaler), so we should create comprehensive theory chapters.

Deliverables

1. Gradient Descent Theory (ml-fundamentals/gradient-descent.md)

   • Mathematical foundations (partial derivatives, learning rate)
   • Batch vs stochastic vs mini-batch variants
   • Convergence analysis
   • Common pitfalls (learning rate selection, local minima)
   • Connection to aprender's SGD implementation

2. Advanced Optimizers Theory (ml-fundamentals/advanced-optimizers.md)

   • Momentum-based methods
   • Adam optimizer (adaptive learning rates)
   • RMSprop, AdaGrad
   • Comparison table of optimizers
   • When to use each optimizer
   • Connection to aprender's Adam implementation

3. Feature Scaling Theory (ml-fundamentals/feature-scaling.md)

   • Why scaling matters (distance-based algorithms)
   • StandardScaler (z-score normalization)
   • MinMaxScaler (range normalization)
   • When to use each method
   • Impact on model performance
   • Connection to aprender's preprocessing module

Acceptance Criteria

• Each chapter has 100+ lines of comprehensive content
• Mathematical formulas included (using LaTeX notation)
• Code examples referencing aprender implementations
• Visual explanations (ASCII art or diagrams)
• Best practices and common pitfalls sections
• References to relevant examples (optimizer_demo.rs, data_preprocessing_scalers.rs)

References

• Existing examples: optimizer_demo.rs, data_preprocessing_scalers.rs
• Source code: src/optim/, src/preprocessing/