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GAN.tar.gz

torch

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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

## Training the CNN classifier
Let’s train this CNN and test its performance in the same way as our fully connected Fashion MNIST classifier.


# Training the CNN classifier
Let’s train this CNN and test its performance in the same way as our fully connected Fashion MNIST classifier.


Learn about training the Fashion MNIST classifier and testing its performance.

The Fashion MNIST CNN Classifier Performance

✨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

The Fashion MNIST CNN Classifier Performance

Training the CNN classifier