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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

## The dataset

Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of **60,000** examples and a test set of **10,000** examples. 

Each example is a **28x28** grayscale image, associated with a label from **10** classes.

Each training and test example is assigned to one of the following labels:

- 0 T-shirt/top
- 1 Trouser
- 2 Pullover
- 3 Dress
- 4 Coat
- 5 Sandal
- 6 Shirt
- 7 Sneaker
- 8 Bag
- 9 Ankle boot 


The dataset for this challenge is taken from [kaggle](https://www.kaggle.com/zalando-research/fashionmnist), which contains the data in a **CSV** format. 

Let's go over the dataset class first. 

## The `FMnistDataset` class

The dataset has similar dimensions to the MNIST dataset. 
The `FMNISTDataset` class is the same as that of the MNIST class defined [here](https://www.educative.io/courses/make-your-first-gan-pytorch/the-mnist-dataset-class).





Run the code below to have a look at the FMNIST images.

> 📝 For simplicity, we have taken a chunk of 140 images from the dataset.

# The dataset

Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of **60,000** examples and a test set of **10,000** examples. 

Each example is a **28x28** grayscale image, associated with a label from **10** classes.

Each training and test example is assigned to one of the following labels:

- 0 T-shirt/top
- 1 Trouser
- 2 Pullover
- 3 Dress
- 4 Coat
- 5 Sandal
- 6 Shirt
- 7 Sneaker
- 8 Bag
- 9 Ankle boot 


The dataset for this challenge is taken from [kaggle](https://www.kaggle.com/zalando-research/fashionmnist), which contains the data in a **CSV** format. 

Let's go over the dataset class first. 

# The `FMnistDataset` class

The dataset has similar dimensions to the MNIST dataset. 
The `FMNISTDataset` class is the same as that of the MNIST class defined [here](https://www.educative.io/courses/make-your-first-gan-pytorch/the-mnist-dataset-class).





Run the code below to have a look at the FMNIST images.

> 📝 For simplicity, we have taken a chunk of 140 images from the dataset.

The Fashion MNIST Dataset Class

✨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 Dataset Class

The dataset