mainpage.h

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/**
 * @mainpage CUDA Neural Networks
 *
 * @section intro_sec Overview
 * Deep neural network implementation from scratch in C++ with CUDA for GPU acceleration. 
 * This project focuses on creating a neural network to classify digits using the MNIST dataset.
 *
 * The main features include:
 * - Matrix and vector operations optimized for GPU
 * - Feedforward neural network with customizable architecture
 * - Training using backpropagation and gradient descent
 * - MNIST digit classification
 *
 * @section requirements_sec System Requirements
 * This project was developed and tested on a system with the following specifications:
 * - OS: Arch Linux x86_64
 * - CPU: Intel i7-9750H (12) @ 4.500GHz
 * - GPU: NVIDIA GeForce GTX 1650 Mobile / Max-Q
 * - Memory: 16GB
 * - NVIDIA Driver Version: 560.35.03
 * - CUDA Version: 12.6
 * - CUDA Compilation Tools: Release 12.6, V12.6.77
 *
 * Please ensure your system meets these requirements or adjust accordingly.
 *
 * @section installation_sec Installation
 * 1. Clone the repository:
 *    ```
 *    git clone https://github.com/juan-esteban-berger/cuda_networks.git
 *    cd cuda_networks
 *    ```
 * 2. Ensure you have the CUDA Toolkit (version 12.6 or compatible) installed on your system.
 * 3. Install the Google Test library if not already present:
 *    ```
 *    sudo pacman -S gtest
 *    ```
 * 4. Build the project:
 *    ```
 *    make
 *    ```
 *
 * @section usage_sec Usage
 * To run the MNIST classifier:
 * ```
 * make run
 * ```
 * This will execute the main program, which trains the neural network on the MNIST dataset 
 * and displays the training progress and final accuracy.
 *
 * @image html demo.gif "Demo of CUDA Neural Networks"
 *
 * @section tests_sec Running Tests
 * To run the test suite:
 * ```
 * make test
 * ```
 * This will execute all the unit tests for the project.
 *
 * @section memcheck_sec Memory Check
 * To run the compute sanitizer memory check:
 * ```
 * make memcheck
 * ```
 * This will use NVIDIA's Compute Sanitizer to check for memory errors in the CUDA code.
 *
 * @section license_sec License
 * This project is licensed under the MIT License - see the LICENSE file for details.
 *
 * @section modifications_sec Modifications
 * If you encounter any issues running the project, you may need to:
 * 1. Adjust the `NVCCFLAGS` in the Makefile to match your GPU architecture.
 * 2. Ensure the paths to the CUDA Toolkit and Google Test library are correct for your system.
 * 3. If you're using a different CUDA version, you might need to update some CUDA function calls or syntax.
 */