Book Image

Mastering PyTorch - Second Edition

By : Ashish Ranjan Jha
4 (1)
Book Image

Mastering PyTorch - Second Edition

4 (1)
By: Ashish Ranjan Jha

Overview of this book

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
Table of Contents (21 chapters)
20
Index

Using GANs for style transfer

So far, we have only looked at DCGANs in detail. Hundreds of different types of GAN models exist already, and many more are in the making. Each of these GAN variants differs by either the application they are catering to, their underlying model architecture, or due to some tweaks in their optimization strategy, such as modifying the loss function. For example, Super-Resolution GAN (SRGAN) are used to enhance the resolution of a low-resolution image. The CycleGAN uses two generators instead of one, and the generators consist of ResNet-like blocks. The Least Squares GAN (LSGAN) uses the mean square error as the discriminator loss function instead of the usual cross-entropy loss used in most GANs.

It is impossible to discuss all of these GAN variants in a single chapter or even a book. However, in this section, we will explore one more type of GAN model that relates to both the DCGAN model discussed in the previous section and the neural style transfer...