This repository presents a comprehensive analysis of FBL (Fast Batch Learning) performance and implementation changes six months after initial deployment. The analysis examines key metrics, performance improvements, and lessons learned from real-world usage.
This presentation-focused repository contains analysis and findings from a six-month retrospective evaluation of FBL implementation. The study covers performance benchmarks, system changes, user feedback, and recommendations for future development.
- Initial FBL implementation baseline metrics
- Six-month performance comparison and analysis
- System architecture changes and optimizations
- User adoption patterns and feedback
- Identified challenges and solutions
- Future roadmap and recommendations
├── docs/ # Documentation and analysis reports
├── source/ # Source materials and data
├── fix_markdown_lists.py # Utility for markdown formatting
└── spelling_converter.py # Text processing utility
The analysis materials are primarily contained in the docs directory. To process or format the presentation materials:
# Fix markdown list formatting
python fix_markdown_lists.py
# Convert text formatting
python spelling_converter.py- Python 3.6+
- Standard library modules (no additional dependencies required)
MIT License