Neural Network

The Neural Network is a comprehensive program designed to simulate and train neural networks for pattern recognition across various datasets. Developed in C++, this program allows users to configure neural networks by specifying critical parameters, including the number of layers, the number of neurons in each layer, and the activation functions used.

A significant feature of this project is its implementation of cross-validation, which divides the dataset into training and testing subsets. This method ensures that the neural network is both trained effectively and tested for accuracy, resulting in a robust model capable of generalizing to new data. The flexibility of the program allows it to be applied to a wide range of data sets, making it a versatile tool for machine learning tasks. Notably, the core functionality of this program was later adapted as a classifier for digit recognition, demonstrating its practical applications.

Technological Details

• Programming Language: C++
• Key Features:
  • Configurable neural network architecture (layers, neurons, activation functions)
  • Cross-validation for robust model training and testing
  • Versatile application across various data sets
  • Adapted as a classifier for digit recognition in subsequent projects
• Software Design:
  • Object-Oriented Programming (OOP) principles
  • Standard Template Library (STL) utilization
• Use Cases:
  • Pattern recognition in diverse datasets
  • Machine learning model development and testing

Predecessor Projects: Numbers Recognition