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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

When we trained networks in [Make Your Own Neural Network](https://www.educative.io/courses/make-your-own-neural-network-in-python), we didn’t really have a way of seeing the progress of that training. We did measure how well the network performed after training, but we didn’t get a sense of how smoothly the training itself had gone, or a sense of whether further training would be helpful.

One way to keep track of training is to monitor the loss.

# Track the loss value during training

We could do this by keeping a copy of the loss value in a list every time it is calculated inside `train()`. This would mean the list would get very big because neural network training is often run for thousands, if not millions, of examples. The MNIST dataset has **60,000** training examples, and we might run several epochs of these. A better way is to keep a copy of the loss value after every **10** training examples. This means we need to keep a count of how often `train()` has been run. 

The following code creates a `counter`, initially set to **0**, and an empty list called `progress` in the constructor of the neural network class.

Visualizing Training

✨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

Visualizing Training