Gain insights into GAN fundamentals and PyTorch. Delve into DCGANs, conditional GANs, image translations, and text-to-image synthesis to master advanced GAN skills for real-world applications.

Packt MBE - Hands-On Generative Adversarial Networks with PyTorch 1.x.png

pytorchgan.tar.gz

pytorch-ch1

code-cgan_mnist

pytorch-spa-cgan-mnist

code-cgan_fashionmnist

code-infogan_mnist

code-widget

pytorch-spa

pytorch-spa-cats

pytorch-spa-seqgan

pytorch-spa-adv

Generative Adversarial Networks (GANs) are a class of machine learning models used to generate data resembling a given dataset. In a GAN, two neural networks, the generator and discriminator, compete. PyTorch is a popular deep learning (DL) framework that is efficient for GAN implementation due to its dynamic computation capabilities.

The course begins with GAN basics, activation functions, and model training best practices. You’ll build your first GAN with PyTorch, exploring DCGANs and conditional GANs. Then, you’ll learn image generation with label info, image-to-image translation with pix2pix and CycleGAN, and image restoration techniques. The course concludes with text-to-image synthesis, sequence synthesis, and 3D model reconstruction, providing a comprehensive understanding of GANs.

This course equips developers with advanced GAN and DL skills. Mastering GANs using PyTorch will enable you to tackle real-world challenges in various domains like image processing and multimedia content generation.

Hands-On Generative Adversarial Networks with PyTorch

Download and install all the required libraries, modules and datasets.

Required Downloads

Getting Started

Generative Adversarial Networks Fundamentals

Best Practices for Model Design and Training

Building Our First GAN with PyTorch

Generating Images Based on Label Information

Image-to-Image Translation and Its Applications

Image Restoration with GANs

Training GANs to Break Different Models

Image Generation from Description Text

Sequence Synthesis with GANs

Reconstructing 3D Models with GANs

Concluding Remarks

Appendix

Required Downloads

Libraries