Complete Recurrent Neural Network library for Processing 4, developed from scratch with custom linear algebra and matrix calculus
This entire library was developed by me from the ground up, including the linear algebra engine, matrix calculus operations, and complete RNN architecture implementation.
RNN Processing AI is a comprehensive Recurrent Neural Network library specifically designed for Processing 4, representing a complete implementation developed entirely from scratch. This project demonstrates deep understanding of neural network fundamentals, mathematical foundations, and software architecture principles.
Unlike using existing libraries, I built every component from the ground up: the linear algebra engine for matrix operations, the complete matrix calculus system for backpropagation, and the entire RNN architecture with forward and backward passes. This approach ensures optimal performance for Processing 4 applications while providing full control over the implementation.
The library is designed to be both educational and practical, making it perfect for understanding neural network internals, experimenting with different architectures, and building AI applications within the Processing creative coding environment.
I implemented a complete linear algebra system from scratch, including matrix operations, vector calculations, and mathematical foundations. This includes matrix multiplication, addition, subtraction, transposition, and specialized operations needed for neural network computations.
The matrix calculus implementation handles all the mathematical operations required for neural network training, including gradient calculations, partial derivatives, and the complete backpropagation algorithm. This was built without relying on external mathematical libraries.
The complete RNN architecture includes forward pass computation, hidden state management, and backward pass for training. The system supports customizable layer sizes, activation functions, and training parameters.
The library implements advanced optimization algorithms including PSO (Particle Swarm Optimization) and Genetic Algorithms for training the neural networks. These evolutionary algorithms are particularly effective for complex optimization problems and have been successfully applied in robotics applications.
Every algorithm was designed with performance in mind, ensuring efficient computation for real-time applications in Processing 4. The implementation avoids unnecessary memory allocations and optimizes mathematical operations.
Perfect for understanding neural network internals, mathematical foundations, and AI principles through hands-on experimentation.
Build interactive AI art, music generation, pattern recognition, and other creative applications within Processing 4.
Platform for testing new neural network architectures, training methods, and AI algorithms in a controlled environment.
Foundation for building specialized AI applications, custom neural networks, and domain-specific machine learning solutions.
This project represents a complete journey from mathematical theory to practical implementation. I started with the fundamental mathematical concepts of linear algebra and calculus, then designed efficient algorithms for each operation, and finally implemented everything in Processing 4's Java environment.
This RNN library was successfully applied in the Follow-Line Simulator project, demonstrating its real-world effectiveness in robotics applications. The combination of the RNN architecture with PSO optimization enabled a robot to learn line-following behavior autonomously.
This practical application showcases the library's capabilities in real-world robotics scenarios, proving that a from-scratch implementation can achieve professional-grade results in autonomous navigation.