Session on Generative Adversarial Networks | GANs

Generative Adversarial Networks (GANs) are a class of artificial intelligence algorithms that consist of two neural networks, the generator and the discriminator, which compete against each other. The generator creates new data samples, such as images or text, while the discriminator tries to differentiate between real and generated samples. Through this adversarial process, GANs can generate highly realistic and diverse outputs, making them a powerful tool for generating synthetic data and creating realistic content like images, videos, and text.

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