What are Generative Adversarial Networks? (GANs explained)

In this video, I aim to answer the age-old question, what are Generative Adversarial Networks? I aim to do it in a viewer and beginner-friendly manner as well. Generative adversarial networks, also referred to as GANs, are algorithmic models that use two neural networks pitted against each other in a game-theoretic friendly scenario, in order to generate synthetic and ai generated instances of data that can pass for real data. Data inputs and outputs can consist of songs, pictures, websites, and even videos. GANs consist of a generator model, that we train to generate new examples, and the discriminator model that tries to classify examples as either real or fake (generated). To try to make this idea easier to understand, I have devised a hypothetical scenario composed of a student and a teacher. GANs are truly one of the most fascinating technologies today, and if there is one thing that I hoped this video accomplished, it’s that I hope you are more interested in the enthralling world of machine learning. I hope you have a better understanding of what Generative Adversarial Networks are as well. Generation AI is a digital brand that makes interesting videos related to AI-generated media technology. At Generation AI, community is at the core of everything we do. Be sure to follow us on social media to contribute to our videos. LEARNING: These two lectures contain answers to most of the questions in the two "it¬ that simple alert" warnings. The lectures are from...