Build Your First Models – Real Python

 Build Your First Models – Real Python

In this step-by-step tutorial, you'll learn all about one of the most exciting areas of research in the field of machine learning: generative adversarial networks.

Generative adversarial networks (GANs) are neural networks that generate material, such as images, music, speech, or text, that is similar to what humans produce.

GANs have been an active topic of research in recent years. Facebook’s AI research director Yann LeCun called adversarial training “the most interesting idea in the last 10 years” in the field of machine learning. Below, you’ll learn how GANs work before implementing two generative models of your own.

In this tutorial, you’ll learn:

  • What a generative model is and how it differs from a discriminative model
  • How GANs are structured and trained
  • How to build your own GAN using PyTorch
  • How to train your GAN for practical applications using a GPU and PyTorch

Let’s get started!

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What Are Generative Adversarial Networks?

Generative adversarial networks are machine learning systems that can learn to mimic a given distribution of data. They were first proposed in a 2014 NeurIPS paper by deep learning expert Ian Goodfellow and his colleagues.

GANs consist of two neural networks, one trained to generate data and the other trained to distinguish fake data from real data (hence the “adversarial” nature of the model). Although the idea of a structure to generate data isn’t new, when it comes to image and video generation, GANs have provided impressive results such as:

neural networks

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