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This project uses a Generative Adversarial Network (GAN) to create synthetic images of handwritten digits from the MNIST dataset. By training the GAN, it learns to generate digit images that closely resemble real handwritten digits. These generated images can be valuable for data augmentation, image synthesis, and various computer vision tasks.