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Model From Scratch with Python\r\n\r\nThis project implements a Generative Adversarial Network (GAN) from scratch to generate handwritten digits using the MNIST dataset.\r\n\r\n## Generative Adversarial Networks (GANs)\r\nGenerator: Generates new data samples.\r\nDiscriminator: Evaluates whether a given data sample is real (from the training data) or fake (generated by the generator).\r\nThe two networks are trained together in a zero-sum game: the generator tries to fool the discriminator, while the discriminator aims to accurately distinguish real from fake data.\r\nA GAN consists of the following key components:\r\nNoise Vector: A random input vector fed into the generator.\r\nGenerator: A neural network that transforms the noise vector into a data sample.\r\nDiscriminator: A neural network that classifies input data as real or fake.\r\n\r\n## Project Structure\r\n- `src/`: Contains the main implementation code\r\n- `outputs/`: Stores generated images during training\r\n- `notebooks/`: Contains exploratory analysis (if any)\r\n\r\n## Requirements\r\n- Python 3.6+\r\n- Keras\r\n- NumPy\r\n- Matplotlib\r\n\r\n## Installation\r\n```bash\r\npip install -r requirements.txt\r\n\r\n## Usage\r\nTo train the model:\r\npython src/train.py\r\n## Results\r\nThe model generates handwritten digits after training. Sample outputs are stored in the outputs/ directory.\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzahramh99%2Fgenerative-ai-from-scratch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzahramh99%2Fgenerative-ai-from-scratch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzahramh99%2Fgenerative-ai-from-scratch/lists"}