Neural Network Implementations from Scratch in Multiple Languages

This repository contains implementations of neural networks written from scratch in various programming languages. Each implementation follows a basic feedforward architecture with backpropagation, with variations in syntax and implementation details specific to each language.

Languages Covered

• Bash: An unconventional approach to neural networks, showcasing how basic shell scripting can be extended to perform mathematical operations.
• C: A low-level, high-performance implementation leveraging direct hardware access and manual memory management for efficiency.
• C#: A high-level implementation using the .NET framework's features to build a neural network efficiently.
• C++: A low-level, memory-optimized implementation, emphasizing performance and manual memory management.
• Java: A robust and scalable implementation, leveraging Java's object-oriented features.
• JavaScript: A neural network running in the browser or Node.js, demonstrating neural computations in a web-friendly environment.
• Python: A clear, easy-to-understand implementation leveraging Python’s simplicity and flexibility.
• Ruby: A Ruby-style implementation showcasing the elegance and readability of the language.
• Rust: A memory-safe, highly performant implementation utilizing Rust's unique ownership system.
• Julia: Julia simplifies neural network implementation with high-level syntax, optimized libraries, and seamless GPU integration.

Features

• Feedforward Neural Networks: Basic multi-layer perceptron models with fully connected layers.
• Backpropagation: Implemented for training the networks using gradient descent.
• Customization: Each language provides its own method of modifying network architecture, hyperparameters, and training options.
• Simple Math Library: In each language, basic math operations like matrix multiplication and activation functions are implemented from scratch to avoid reliance on external libraries.

Structure

Each folder corresponds to a specific language. Inside each folder, you'll find:

• The source code for the neural network.
• A brief explanation of how to run the code.
• Example datasets used (if applicable).

Getting Started

Prerequisites

Ensure you have the appropriate environment set up for each language:

• Bash: Works on any Unix-based shell (e.g., Linux, macOS).
• C#: Requires .NET SDK (v5.0 or above).
• C++: Requires a C++ compiler (e.g., g++, clang).
• Java: Requires JDK (v8 or above).
• JavaScript: Node.js for server-side, or a modern web browser.
• Python: Python 3.x.
• Ruby: Ruby (v2.6 or above).
• Rust: Requires the Rust toolchain (cargo).
• Julia: Requires Julia to be downloaded from Microsoft Store or its official website.

Running the Neural Networks

For each language, navigate to the corresponding folder and follow the instructions in the README.md file present in each directory.

Example for Python:

cd python
python neural_network.py

Example for C++:

cd cpp
g++ neural_network.cpp -o neural_network
./neural_network

License

This project is licensed under the MIT License - see the LICENSE file for details.