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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

# CPU vs. GPU
All computers have a **CPU**, the main component that does most of the work. It runs our Python code, for example. We can think of it as the brains of our computer. CPU stands for **central processing unit**, but nobody really thinks about those words anymore. CPUs are designed to be general-purpose, and good at many different kinds of tasks. 


Those graphics cards we mentioned earlier contain a **GPU**, or **graphics processing unit**. Unlike a general-purpose CPU, a GPU is designed to perform specific tasks and do them well. One of those tasks is to carry out arithmetic, including matrix multiplication, in a highly parallel way.

The following picture illustrates this key difference between a CPU and a GPU.


- If we have lots of calculations to do, a CPU will work through them one after another. Modern CPUs might be able to use $2$ or $4$, or maybe even $8$ or $16$ cores to work through the calculations. Only very recently have consumer CPUs contained $64$ cores.

- GPUs have many more arithmetic cores. Having thousands is fairly common today. This means a huge workload can be split amongst all these cores and the job can be done quickly.

# CUDA
 



Learn about the difference between a CPU and a GPU, what can GPU provide, and the Cuda - a software framework that enables GPU.

Nvidia CUDA

✨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

Nvidia CUDA

CPU vs. GPU