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Gain insights into Generative Adversarial Networks with PyTorch. Delve into neural networks, adversarial training, and building GANs for image generation, including conditional GANs for class-specific data.

GAN.tar.gz

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This course is an introduction to Generative Adversarial Networks (GANs) and a practical step-by-step tutorial on making your own with PyTorch. Through this course, you will learn how to build GANs with industry-standard tools.

In the first section, you will dive into PyTorch and refresh your understanding of neural networks by building a simple image classifier. In the second section of this course, you will explore the idea of adversarial training and build progressively more sophisticated GANs; first by learning a simple 1010 pattern, then monochrome images of handwritten digits, and finally full-color images of faces.

The third, and last section, extends the core GAN concept, applying it to convolutional neural networks, and developing a conditional GAN for generating data of the desired class.

By the end of this course, you will have a solid understanding of how to build GANs for your machine learning projects.

Make Your First GAN Using PyTorch

- **Classification** is a _reduction of data_. Classifying neural networks will reduce many input values to a much smaller number of output values, one for each class.

- **Generation** is normally an _expansion of data_. A generative neural network will expand a small number of input seed values to a much larger number of output values, image pixel values for example.

- A generative adversarial network **(GAN)** has two neural networks, a **generator** and a **discriminator**, set to compete against each other as adversaries. The discriminator is trained to classify data from a training set as real, and data from the generator as fake. The generator is trained to create data that looks real enough to fool the discriminator.

- **Designing and training GANs** to reliably succeed is **difficult**. GANs are new and the underlying theory describing how they work, and why training can fail, is not yet mature.

- The standard **GAN training loop** has three steps: 
  1.  Train the discriminator with a real data example.
  2.  Train the discriminator with a generated data example.
  3.  Train the generator to get a correct classification from the discriminator.

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Let's summarize all about the basic idea of GANs.

📚Learning Points in The GAN Idea

✨Introduction

✨PyTorch Basics

🖥️ Project: Computation Graph and Gradient Calculation

✨First PyTorch Neural Network

✨Refinements

🖥️ Project: Fashion MNIST Classification Model

✨CUDA Basics

Make Your First GAN: Part 1 - Exam

✨The GAN Idea

✨Simple 1010 Pattern

🖥️ Project: A Simple 01110 Pattern GAN

✨Handwritten Digits

🖥️ Project: Fashion MNIST GAN

✨Human Faces

Make Your First GAN: Part 2 - Exam

✨Convolutional GANs

🖥️ Project: A Fashion MNIST CNN

✨Conditional GANs

🖥️ Project: Fashion MNIST Conditional GAN

Make Your First GAN: Part 3 - Exam

✨Conclusion

✨Appendix

📚Learning Points in The GAN Idea