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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

What is Contrastive Learning?

The idea of understanding an image is to get an image of a particular kind (say a dog) and then we can recognize all other dogs by reasoning that they share the same representation or structure. For example, if you show a child who is not yet able to talk or understand language (say, less than 2 years old) a picture of a dog (or a real dog for that matter) and then give them a pack of cards with a collection of animals, which includes dogs, cats, elephants, and birds, and ask the child which picture is similar to the first one, it is most likely that the child could easily pick the card with a dog on it. And the child would be able to do so even without you explaining that this picture equals "dog" (in other words, without supplying any new labels).

You could say that a child learned to recognize all dogs in a single instance and with a single label! Wouldn't it be awesome if a machine could do that as well? That is exactly what contrastive...

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