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Generative Adversarial Networks (GANs), developed in 2014 by Ian Goodfellow and colleagues, are a model in deep learning that generates revolution. GANs consist of a structure in which two opposing neural networks train together to improve their learning processes. One of these networks is called the generator, and the other is called the discriminator. The primary goal of GANs is to enable the generator to produce data similar to the real data while allowing the discriminator to determine wheth

Generative Adversarial Networks (GANs) are a model developed in 2014 by Ian Goodfellow and colleagues that revolutionized the field of deep learning. GANs operate through a structure in which two opposing neural networks learn by working birlikte. One of these networks is called the generator, and the other is called the discriminator. The primary goal of GANs is to enable the generator to produce data that resembles gerçek data, while the discriminator learns to determine whether the data is sa