Hi there! 👋 I'm a passionate Python Developer with a knack for creating meaningful and impactful projects. My primary focus revolves around data analysis, financial simulations, and building intuitive tools and applications. Let's dive into what makes this space awesome!

• Spiking Neural Networks (SNN):
  • Implementation with SNNTorch
  • Hybrid MLP-SNN architectures
  • Leaky Integrate-and-Fire neurons
• Evolutionary Algorithms:
  • Performance-based selection
  • Adaptive mutation
  • Population-based training
• Reinforcement Learning:
  • Multi-agent environments
  • Hierarchical reward systems
  • Intrinsic curiosity mechanisms

• Hybrid Architecture:
  • PyTorch + SNNTorch integration
  • MVC pattern for complex systems
  • Residual blocks & multi-head attention
• Optimization:
  • NumPy vectorization
  • PyTorch parallelism
  • RL resource management
• Testing & Validation:
  • Unit tests for NN components
  • Evolutionary policy validation
  • Training metrics monitoring

• Autonomous Systems:
  • Spatial memory navigation
  • Hierarchical decision-making
  • Limited resource management
• Experimental Research:
  • Transfer learning between agents
  • Predator-prey dynamics
  • Scalable dynamic environments

• Core Concepts: Variables, operators, conditional structures, loops
• Data Structures: Lists, dictionaries, tuples for versatile data storage
• Functions: Building reusable tools with parameters and return values
• Error Handling: Robust validations using try/except
• String Manipulation: Formatting data for user-friendly outputs

• Object-Oriented Programming (OOP):
  • Modular architecture with MVC patterns
  • Specialized classes with inheritance
  • Use of @property decorators for advanced validations
• Scientific Libraries:
  • NumPy: Handling financial calculations like future value
  • Pandas: Tracking and analyzing simulation histories
  • SciPy: Optimizing complex calculations
• Testing Frameworks:
  • pytest: Automating unit and functional tests
  • unittest: Ensuring UI functionality
• Design Patterns: Implementing Factory Method, Observer, and Strategy for scalable solutions

• Concurrency & Parallelism:
  • Threading for heavy computations
  • Asyncio for efficient I/O
• Financial Integrations:
  • Pulling real-time market data from APIs like Yahoo Finance
• Interactive Visualizations:
  • Plotly: Stunning, interactive charts
  • Dash/Streamlit: Web-friendly dashboards
• Type Hints & Documentation:
  • Clear and maintainable codebases with type annotations and docstrings

🧠⚡ SNN Evolutionary AI Testbed
An experimental AI project testing Spiking Neural Networks (SNN) in complex multi-agent environments using evolutionary algorithms.

📌 Overview
This project simulates autonomous agents with hybrid neural architectures (MLP + SNN) that learn to collect trash efficiently while managing limited battery resources. It serves as a testbed for:

• Evolutionary training methods
• Spiking Neural Networks in complex environments
• Multi-agent reinforcement learning dynamics
• Memory-augmented navigation strategies

A powerful Compound Interest Calculator built with Python, designed for both educational and practical use cases.
It features:

• Customizable simulations
• Historical data tracking
• Exportable reports in PDF format

1. Add persistence with SQLite
2. Implement exportable reports in PDF
3. Introduce internationalization (i18n) for global usability

• Build a loan simulator
• Create an investment comparison tool
• Develop a portfolio tracking bot

• Deploy web-based versions using FastAPI or Streamlit
• Build mobile apps using Kivy or BeeWare
• Establish CI/CD pipelines with GitHub Actions

• 📧 Email: [email protected]
• 💼 LinkedIn: David Rodrigues Resende

Thank you for visiting! 😊 Let's collaborate and build something exceptional together. 🚀✨