Aim

To study Generative Adversarial Networks (GANs) by implementing a Deep Convolutional GAN (DCGAN) on the MNIST dataset for image generation. To understand the step-by-step adversarial training process between the Generator and Discriminator. To analyze how hyperparameter tuning affects generated image quality, mode collapse, training progress, and the continuous latent manifold through interactive and epoch-wise visualizations.