Generate Handwritten Digit Images using DCGAN 🧠✨

This project uses a Deep Convolutional Generative Adversarial Network (DCGAN) to generate realistic handwritten digit images similar to the MNIST dataset.

📝 Project Overview

This notebook demonstrates the power of DCGANs in generating synthetic images that resemble handwritten digits. The model consists of:

• A Generator that learns to create realistic images.
• A Discriminator that learns to distinguish between real and fake images.

Both networks are trained in an adversarial fashion, improving each other over time.

🧰 Technologies Used

• Python 🐍
• TensorFlow / Keras 🧠
• NumPy 📊
• Matplotlib 📈
• MNIST Dataset 🖋️

🧪 How to Run

1. Clone the repository or download the .ipynb file.
2. Install the required packages (e.g., tensorflow, numpy, matplotlib).
3. Open the Jupyter notebook:

   jupyter notebook Generate_handwritten_digit_images_DCGAN.ipynb

4. Run the cells in order to train the DCGAN and generate digit images.

📸 Sample Output

After training, the generator will produce handwritten digits like:

[Image of generated digits can be added here]

🧠 Concepts Covered

• Generative Adversarial Networks (GANs)
• Deep Convolutional Networks
• Image generation from noise
• Binary crossentropy loss and adversarial training

📂 Dataset

• MNIST Handwritten Digits Dataset

📌 Future Improvements

• Tune hyperparameters for better image quality
• Add conditional GAN for class-specific digit generation
• Train on more complex datasets like Fashion MNIST or CIFAR-10

🤝 Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

📄 License

This project is open-source and available under the MIT License.

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“GANs don’t just generate data; they generate possibilities.” 🚀