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Deep Learning with PyTorch Lightning

You're reading from   Deep Learning with PyTorch Lightning Swiftly build high-performance Artificial Intelligence (AI) models using Python

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781800561618
Length 366 pages
Edition 1st Edition
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Authors (2):
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Dheeraj Arremsetty Dheeraj Arremsetty
Author Profile Icon Dheeraj Arremsetty
Dheeraj Arremsetty
Kunal Sawarkar Kunal Sawarkar
Author Profile Icon Kunal Sawarkar
Kunal Sawarkar
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Kickstarting with PyTorch Lightning
2. Chapter 1: PyTorch Lightning Adventure FREE CHAPTER 3. Chapter 2: Getting off the Ground with the First Deep Learning Model 4. Chapter 3: Transfer Learning Using Pre-Trained Models 5. Chapter 4: Ready-to-Cook Models from Lightning Flash 6. Section 2: Solving using PyTorch Lightning
7. Chapter 5: Time Series Models 8. Chapter 6: Deep Generative Models 9. Chapter 7: Semi-Supervised Learning 10. Chapter 8: Self-Supervised Learning 11. Section 3: Advanced Topics
12. Chapter 9: Deploying and Scoring Models 13. Chapter 10: Scaling and Managing Training 14. Other Books You May Enjoy

Getting started with transfer learning

Transfer learning has many interesting applications, with one of the most fascinating being converting an image into the style of a famous painter, such as Van Gogh or Picasso.

Figure 3.1 – Image credit: A neural algorithm of artistic style (https://arxiv.org/pdf/1508.06576v2.pdf)

The preceding example is also known as Style Transfer. There are many specialized algorithms for accomplishing this task, and VGG-16, ResNet, and AlexNet are some of the more popular architectures.

In this chapter, we will start with the creation of a simple image classification model using ResNet-50 architecture on the PCam dataset, which contains image scans of cancer tissues. Later, we will build a text classification model that uses Bi-directional Encoder Representations from Transformers (BERT).

In both examples in this chapter, we will make use of a pre-trained model and its weights and fine-tune the model to make it work...

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