kaggle_username

kaggle_key

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

torch

J03T

J03O

J03P

J04AF

J04OF

J04N

J04CR

J05UCP

J07TMG

J07TTMG

J16TG

J16CGR

JAppebdixB

JAppebdixD

SPA04

SPA16

SPA13

SPA09

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 **MNIST dataset** is a **well-known set of images** often used to measure and compare the performance of machine learning algorithms. It contains **60,000** images for training a machine learning model, and **10,000** images for testing its performance.

- The **MNIST image** is **28 by 28**, or **784** pixel values. That means the first layer of our neural network must have 784 nodes. We have defined a hidden layer with 200 nodes. The outer layer has 10 nodes, i.e., one node for each of these 10 possible classes.

- The **neural network class** `Classifier` is defined  using PyTorch's `torch.nn` library. The class contains:
  - The neural network architecture.
  - The **`forward` function** to perform forward propagation.
  - The **`train` function** to optimize network weights during back propagation.
  - The **`plot_progress` function** to visualize loss during training.

- The **MNIST Dataset** class contains functions to load and use data in the PyTorch way.
  - The **`__len__()` function** simply returns the length of the dataframe.
  - The **`__getitem__()`** function extracts a label from the index-th item in the dataset.
  - The **`plot_image()` function** plots the image.


- **Training** the classifier involves:
  - Creating an object of the neural network `Classifier` class. 
  - Running the `train` function through every record of the sample `epoch` times.

- The **performance of the network can be tested** by using the unseen test data sample and calling a `forward` function of the neural network class.

- If we want to know **how well our classifier performed** it has to work through all the 10,000 images in the MNIST test dataset and keep a count of how many it got right.  

_____



Let's summarize how to make a first PyTorch neural network.

📚Learning Points in First PyTorch Neural Network

✨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 First PyTorch Neural Network