Learn What is a Generative Adversarial Network work and their impact on artificial intelligence and machine learning. A Generative Adversarial Network (GAN) is a class of machine learning models that involves two neural networks, a generator and a discriminator, working in tandem to produce new, realistic data. The generator creates synthetic data (such as images or text), while the discriminator evaluates its authenticity against real-world data. Over time, this adversarial process leads to the creation of highly accurate and realistic outputs. GANs are widely used in various applications, including image generation, video synthesis, and creating realistic simulations. They have also found applications in industries such as entertainment, gaming, and healthcare. By pushing the boundaries of creativity and innovation, GANs demonstrate the potential of AI in generating high-quality, artificial content.