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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


## Author's note

I will have failed in my mission if any reader has trouble understanding what GANs are, and they are trained. I will also have failed if you haven’t been able to build your own simple GAN. 


### Further readings

Additional discussion and responses to interesting reader questions are written up on the blog that accompanies this course and the previous course: [Make Your Own Neural Network in Python](https://www.educative.io/courses/make-your-own-neural-network-in-python).


### Final notes

Finally, I’d really recommend joining your local machine learning or algorithmic art community. Learning in a supportive group is much more effective, and it’s fun sharing your work and being inspired by what others come up with. 

Have fun!
🎉

----


# Author's note

I will have failed in my mission if any reader has trouble understanding what GANs are, and they are trained. I will also have failed if you haven’t been able to build your own simple GAN. 


## Further readings

Additional discussion and responses to interesting reader questions are written up on the blog that accompanies this course and the previous course: [Make Your Own Neural Network in Python](https://www.educative.io/courses/make-your-own-neural-network-in-python).


## Final notes

Finally, I’d really recommend joining your local machine learning or algorithmic art community. Learning in a supportive group is much more effective, and it’s fun sharing your work and being inspired by what others come up with. 

Have fun!
🎉

----

Author's guidelines and ending notes about the course.

Appendix 0: Author's Notes

✨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

Appendix 0: Author's Notes

Author’s note