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Mastering PyTorch

You're reading from   Mastering PyTorch Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781801074308
Length 558 pages
Edition 2nd Edition
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Author (1):
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Ashish Ranjan Jha Ashish Ranjan Jha
Author Profile Icon Ashish Ranjan Jha
Ashish Ranjan Jha
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Table of Contents (21) Chapters Close

Preface 1. Overview of Deep Learning Using PyTorch 2. Deep CNN Architectures FREE CHAPTER 3. Combining CNNs and LSTMs 4. Deep Recurrent Model Architectures 5. Advanced Hybrid Models 6. Graph Neural Networks 7. Music and Text Generation with PyTorch 8. Neural Style Transfer 9. Deep Convolutional GANs 10. Image Generation Using Diffusion 11. Deep Reinforcement Learning 12. Model Training Optimizations 13. Operationalizing PyTorch Models into Production 14. PyTorch on Mobile Devices 15. Rapid Prototyping with PyTorch 16. PyTorch and AutoML 17. PyTorch and Explainable AI 18. Recommendation Systems with PyTorch 19. PyTorch and Hugging Face 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...

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