Add comprehensive PROJECT_OVERVIEW.md documentation

[Copilot]

Created exhaustive technical documentation for the MNIST digit classifier neural network implementation.

Changes

• PROJECT_OVERVIEW.md (new, 1,542 lines)

  • Neural network architecture: 3-layer feedforward (784→100→10) with sigmoid activations
  • Mathematical foundations: forward/backpropagation, cross-entropy loss, L2 regularization, L-BFGS-B optimization
  • File-by-file breakdown: detailed analysis of Model.py, Prediction.py, RandInitialise.py, main.py
  • Training pipeline: initialization, convergence behavior, expected performance (95-97% test accuracy)
  • Visualization explanations: accuracy curves, weight maps, layer activations
  • Installation, usage, troubleshooting, and extension guides
  • Appendix: mathematical notation, complexity analysis O(m×n×h), glossary

• README.md (updated)

  • Added prominent link to comprehensive documentation
  • Added Quick Start section for immediate onboarding

Documentation Structure

16 major sections covering:

• Technical architecture and system design
• Complete mathematical derivations with formulas
• Code execution flow and function hierarchies
• Performance metrics and per-class accuracy analysis
• Advanced features: architecture modifications, alternative activations, regularization techniques
• Future improvements categorized by implementation difficulty

Target audience: beginners learning neural networks from scratch through advanced practitioners extending the implementation.

Original prompt

Create an extreamly deatiled overview of this project

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