[Part 9/20] Creating and Training Generative Adversarial Networks in PyTorch
Deep Learning with PyTorch — Part 9/20
Table of Contents
1. Understanding the Basics of GANs
2. Setting Up Your PyTorch Environment
3. Designing the Generator and Discriminator Networks
4. Implementing the Training Loop for GANs
5. Evaluating GAN Performance and Troubleshooting Common Issues
6. Advanced Techniques in GANs with PyTorch
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1. Understanding the Basics of GANs
Generative Adversarial Networks (GANs) are a class of generative models designed to create new data instances that resemble your training data. Here’s what you need to know to get started:
What are GANs? At their core, GANs consist of two neural networks: the Generator and the Discriminator. The Generator creates new data instances, while the Discriminator evaluates them against the real data.
