VectorForgeML

🚀 VectorForge ML

High-Performance Machine Learning Framework in C++

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📌 Overview

VectorForge ML is a machine learning framework built from scratch in C++, focused on understanding the mathematical foundations of ML while designing a scalable and performance-oriented system.

Unlike traditional ML libraries that abstract away implementation details, this project emphasizes:

• Manual implementation of core algorithms
• Mathematical clarity
• Optimization techniques
• Clean modular architecture
• Progressive system scaling

The goal is to evolve this project from a beginner-level implementation into a research-ready ML engine with performance benchmarking and optional R integration.

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🎯 Project Vision

This project is being built in structured phases:

🌱 Beginner → Basic Linear Regression
🌿 Intermediate → Logistic Regression, K-Means
🌳 Advanced → Neural Networks, Optimization, Multithreading
📊 Research Level → Benchmarking + IEEE Paper

We are building this step-by-step to ensure deep understanding and strong engineering foundations.

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🧠 Algorithms (Current & Planned)

✅ Implemented

• Linear Regression (Gradient Descent)

🔄 In Progress

• Logistic Regression
• Matrix Utility Module

🚀 Planned

• K-Means Clustering
• Neural Network (MLP)
• Optimizer Abstractions
• L2 Regularization
• Early Stopping
• Multithreaded Matrix Computation
• R Integration via Rcpp

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🏗 Project Structure

VectorForgeML/
│
├── src/                # Core C++ source files
├── include/            # Header files
├── data/               # Sample datasets
├── benchmarks/         # Performance comparison tests
├── docs/               # Mathematical derivations & notes
└── README.md

The architecture is modular to allow scalable growth.

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⚙️ How to Build

Using g++

g++ src/main.cpp src/linear_regression.cpp -o vectorforge
./vectorforge

Load Framework

install.packages("remotes", repos="https://cloud.r-project.org")

remotes::install_github("mohd-musheer/VectorForgeML")

library(VectorForgeML)
ls("package:VectorForgeML")

cat("Loading dataset...\n")

df <- read.csv(system.file("dataset","cars.csv", package="VectorForgeML"))

Kaggle Notebooks :-

Linear Regression : https://www.kaggle.com/code/almusheer/linear-regression-vectorforgeml

PipeLine : https://www.kaggle.com/code/almusheer/pipeline-vectorforgeml

Logistic Regression : https://www.kaggle.com/code/almusheer/logistic-regression-vectorforgeml

(Advanced build system using CMake will be added in future versions.)

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📊 Performance Philosophy

VectorForge ML is designed with:

• Efficient memory usage
• Manual vector operations
• Gradient optimization techniques
• Future support for multithreading
• Benchmark comparison with native R implementations

Performance metrics will be published in later versions.

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📘 Educational Purpose

This project is designed to:

• Strengthen understanding of ML mathematics
• Improve C++ engineering skills
• Build systems-level optimization knowledge
• Prepare for ML/Backend/Research roles

It is not meant to replace production ML libraries — it is meant to deeply understand and build one.

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🤝 Contributing

Contributions are welcome and encouraged.

If you’d like to contribute:

1. Fork the repository
2. Create a new branch
3. Implement your feature or improvement
4. Submit a Pull Request

You can contribute by:

• Improving algorithm efficiency
• Adding new ML models
• Writing unit tests
• Improving documentation
• Adding benchmark scripts
• Suggesting architectural improvements

Please keep code clean and well-documented.

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📌 Contribution Guidelines

• Follow consistent naming conventions
• Write clear comments explaining math
• Keep functions modular
• Avoid unnecessary complexity
• Test before submitting pull requests

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📜 License

This project is open-source and available under the MIT License.

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👨‍💻 Author

Musheer
Machine Learning & Systems Enthusiast

Focused on building ML systems from scratch for deep understanding and performance optimization.

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