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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

- State of the art **image classifying networks** take advantage of the fact that **meaningful image features are localized**, and that **recognizable objects are composed of a hierarchy of low-level detail features**, which combine to form medium-level features, which themselves combine to form high-level objects. 

- **Convolutions** apply a **kernel** over an image to output **feature maps**. Specific kernels can pick out specific localized patterns in an image. 

- **Convolution layers** in a neural network can **learn good kernels for achieving the given task**. That is, the network learns which image features are most useful without us engineering those features directly. Neural networks that use convolutional layers generally perform better at image classification than equivalent fully connected networks. 

- Where a convolution reduces data, the equivalently configured **transposed convolution reverses this reduction**, making it ideal for generative networks.

- GANs based on convolutional networks construct images by **composing low-level features into medium-level features, which are composed into high-level features**. Experiments show the images they produce are of higher quality than equivalent fully connected GANs.

- **Convolution GANs** require **less memory than fully connected GANs**. This can be a factor when working with medium to large-sized images within the constraints of GPU memory. We saw a 5 times reduction in memory consumption.

- One **disadvantage of convolutional generators** is that they can lead to images composed of **unmatching elements**, for example, faces with mismatched eyes. This is because the information flow through a convolutional network is intentionally localized, and global relationships aren’t learned. 


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📚Learning Points in Convolutional GANs

✨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 Convolutional GANs