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ML_Simulator

Overview

ML_Simulator is a fully customizable Machine Learning IDE that empowers you to design, train, and test neural network models through an intuitive drag-and-drop interface. Built using Python and PyQt5, the IDE provides a visual environment for creating complex models with ease—ideal for both rapid prototyping and in-depth experimentation.

Features

  • Drag-and-Drop Model Creation: Build your neural network by visually connecting nodes.
  • Customizable Components: Create and modify nodes with customizable settings such as activation functions, biases, and neuron counts.
  • Interactive Network Visualization: Watch your network come to life with real-time updates as connections are made and weights are randomized.
  • Training and Evaluation: Train your models using built-in backpropagation and feedforward routines, with support for both SGD and Adam optimizers.
  • Dynamic UI Elements: Enjoy a polished, interactive UI that lets you add training data via a table interface, adjust node settings, and toggle between different visual representations.

Installation

Prerequisites

  • Python 3.x: Ensure you have Python 3 installed on your system.
  • Required Libraries:

Clone the Repository

Open your terminal and execute:

git clone https://github.com/YourUsername/ML_Simulator.git
cd ML_Simulator

Install Dependencies

Install the necessary packages by running:

pip install -r requirements.txt

Usage

To launch ML_Simulator, simply run:

python3 main.py

Once started, use the drag-and-drop interface to add nodes, set up network connections, and train your machine learning models interactively.

Roadmap

  • Real-Time Training Visualization: Add live training updates to monitor progress as the model learns.
  • Prebuilt Model Templates: Integrate templates for popular architectures such as CNNs, RNNs, etc.
  • Model Export/Import: Enable features for saving and loading trained models for future use.
  • Enhanced UI/UX: Continuously refine the interface to boost usability and performance. TODO

Contributing

Contributions are welcome! To get involved:

  1. Fork the repository.
  2. Create a feature branch.
  3. Submit a pull request with your enhancements.

License

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