Syntropy refers to the tendency to create order from chaos — the opposite of entropy. In signal processing and machine learning, we extract meaningful patterns from noisy data, impose structure on complexity, and transform raw information into knowledge. This repository embodies that principle: turning the chaos of learning into structured understanding through hands-on experimentation.
GCPDS stands for Grupo de Control y Procesamiento Digital de Señales (Digital Signal Processing and Control Group) at the Universidad Nacional de Colombia. The group focuses on signal processing, control systems, and machine learning applications.
This is a learning framework for digital signal processing (DSP) and IQ signal analysis, built with Python and Jupyter notebooks. It includes:
- Study notebooks: A structured curriculum covering Python functions for IQ signals, NumPy arrays, indexing, broadcasting, and power analysis
- IIR filter exercises: Parallel implementations by multiple students, exploring filter design and GNU Radio integration
- Quality assurance: NBQA linting pipeline and ML notebook audit workflow
- Agent-driven generation: Prompts and contracts for automated notebook creation
The repository serves as both a teaching tool and a research platform for exploring how AI agents can assist in generating educational materials.