Generative-Adversarial-Network

Developed a GAN model using TensorFlow to produce realistic images that resemble a custom dataset. • Created a generator model utilizing deep neural networks to craft realistic images from random noise.Simultaneously, designed a discriminator model to effectively differentiate between synthesized and genuine images. • Employed GAN architecture, Convolutional Neural Networks (CNNs), Batch Normalization, Leaky ReLU activation, and Binary Cross-Entropy loss.