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

Neural Style Transfer

In the previous chapter, we started exploring generative models using PyTorch. We built machine learning models that can generate text and music by training the models without supervision on text and music data, respectively. We will continue exploring generative modeling in this chapter by applying a similar methodology to image data.

We will mix different aspects of two different images, A and B, to generate a resultant image, C, that contains the content of image A and the style of image B. This task is also popularly known as neural style transfer because, in a way, we are transferring the style of image B to image A to achieve image C, as illustrated in Figure 8.1:

Figure 7.1 – Neural style transfer example

Figure 8.1: Neural style transfer example

First, we will briefly discuss how to approach this problem and understand the idea behind achieving style transfer. Using PyTorch, we will then implement our own neural style transfer system and apply it to a pair of images. Through this...