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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

Chapter 6: Deep Generative Models

It has always been the dream of mankind to build a machine that can match human ingenuity. While the word intelligence comes with various dimensions, such as calculations, recognition of objects, speech, understanding context, and reasoning; no aspects of human intelligence make us more human than our creativity. The ability to create a piece of art, be it a piece of music, a poem, a painting, or a movie, has always been the epitome of human intelligence, and people who are good at such creativity are often treated as "geniuses." The question that remains fully unanswered is, can a machine learn creativity?

We have seen machines learn to predict images using a variety of information and sometimes even with little information. A machine learning model can learn from a set of training images and labels to recognize various objects in an image; however, the success of vision models depends on their capability for vast generalizations –...

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