This repository contains a pure CUDA C++ implementation of a Multilayer Perceptron (MLP) neural network with a Linear Regression Dataset with Apache 2 license on which the network attained a loss of 0.07. By building from the ground up, we gain a deep understanding of the inner workings of neural networks and the performance benefits of GPU acceleration.

Disclaimer:

• This project is for educational purposes and may not be optimized for production use.

Features:

• Pure CUDA C++ implementation: No external libraries or frameworks.
• GPU acceleration: Leverage the power of GPUs for high performance.
• Modular design: Clear separation of concerns for maintainability.

Getting Started:

• Clone the repository: Bash git clone https://github.com/inventwithdean/CUDA_MLP.git

• Set up CUDA environment: Ensure you have a CUDA-capable GPU and the necessary CUDA toolkit installed.

• Compile the code: Use a CUDA-compatible compiler to build the project.

• Run the executable: Execute the generated binary to run the MLP.

Structure:

• include: Header files for classes and functions along with some kernels for MSE and ReLUs.
• kernels: CUDA kernels for matrix related calculations
• main.cu: Main file where dataset is loaded and Optimization loop happens
• dataset.cu: A very simple and lightweight csv Dataset Loader class

Future Improvements:

• Implementing Softmax and Convolutional layers.
• Add optimization techniques like momentum.
• Explore different activation functions and network architectures.
• Improve performance through kernel tuning and optimization.

Contributing:

Feel free to contribute to this project by:

• Submitting bug reports
• Suggesting new features
• Improving the code
• Writing documentation

Let's dive into the world of deep learning together!

License:

MIT