Artificial Neural Network Simulation and Python Code Editor

This project is designed to enable users to experience the working principles of artificial neural networks in an interactive environment and to learn the Python programming language in practice. The project includes:

• Simulation Environment: Users can observe how artificial neural networks react by changing parameters.
• Code Editor: Users can run Python codes in real time.

Features

1. Interactive Neural Network Simulation

   • Visualize neural network architectures.
   • Adjust parameters such as learning rate, number of layers, activation functions, etc.
   • Observe the impact of parameter changes on network performance.

2. Integrated Python Code Editor

   • Write and execute Python code directly within the environment.
   • Real-time feedback and error checking.
   • Tutorials and examples to help users learn Python programming.

Objectives

• Educational Tool: Provide a hands-on learning experience for understanding neural networks and Python programming.
• Experimentation Platform: Allow users to experiment with different neural network configurations and see the results instantly.
• Skill Development: Help users develop practical skills in Python programming and neural network design.

Usage

1. Simulation Environment

   • Select the neural network architecture.
   • Modify parameters such as learning rate, activation functions, number of layers, etc.
   • Run the simulation to see how the changes affect the network.

2. Code Editor

   • Open the integrated Python code editor.
   • Write or paste Python code related to neural networks or other applications.
   • Execute the code and view results immediately within the environment.

Getting Started

1. Clone the repository:

   git clone https://github.com/rumeysacelik/neural_network_interface.git