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Bookmark or Share- A Gentle Introduction to Generative Adversarial Networks (GANs)Generative Adversarial Networks, or GANs, are a deep-learning-based generative model. More generally, GANs are a model architecture for training a generative model, and it is most common to use deep learning models in this architecture.
- Generative adversarial network - WikipediaA generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative AI. The concept was initially developed by Ian Goodfellow and his colleagues in June 2014.
- What is GAN? Generative Adversarial Networks ExplainedWhat is GAN? GAN stands for Generative Adversarial Network. It’s a type of machine learning model called a neural network, specially designed to imitate the structure and function of a human brain. For this reason, neural networks in machine learning are sometimes referred to as artificial neural networks (ANNs).
- Understanding Generative Adversarial Networks (GANs)Generative Adversarial Networks belong to the set of generative models. It means that they are able to produce / to generate (we’ll see how) new content. To illustrate this notion of “generative models”, we can take a look at some well known examples of results obtained with GANs.
- Introduction | Machine Learning | Google for DevelopersThis course covers GAN basics, and also how to use the TF-GAN library to create GANs. Course Learning Objectives. Understand the difference between generative and discriminative models....
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