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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

- When working with new data, or building a new workflow, it is good practice to **preview the data** to ensure it was loaded and transformed properly.

- **PyTorch** can do a lot of the work in a **machine learning task** for us. To take advantage of this, we need to work with PyTorch’s machinery. For example, neural networks should be derived from PyTorch’s `nn.Module` class. 

- It is good practice to **visualize loss values** so we can see how well training went.

- The **mean squared error loss is suited to regression tasks** where the output can be in a continuous range. The **binary cross entropy loss is better suited to classification tasks** where the output should be 1 or 0, for true and false.

- The traditional **sigmoid activation function suffers from vanishing gradients** for large input values. This leads to poor feedback signals for training a network. The **ReLU activation function solves this by having good gradients for positive inputs**. The **LeakyReLU improves this by also having a small non-zero gradient for negative values**.

- The `Adam` optimizer uses momentum to **overcome local minima** and **maintains individual learning rates for each learnable parameter**. For many tasks, it performs better than the simple `SGD` optimizer.

- **Normalization** can **stabilize the training** of neural networks. It is common to normalize a network’s initial weights. Normalizing signal values as they pass through a neural network with `LayerNorm` can improve performance.


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Let's summarize all that we have learned about refinements.

📚Learning Points in Refinements

✨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 Refinements