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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

Technical requirements

The code for this chapter has been developed and tested on a macOS with Anaconda or in Google Colab with Python 3.6. If you are using another environment, please make the appropriate changes to your environment variables.

In this chapter, we will primarily be using the following Python modules, mentioned with their versions:

  • PyTorch Lightning (version 1.5.2)
  • Seaborn (version 0.11.2)
  • NumPy (version 1.21.5)
  • Torch (version 1.10.0)
  • pandas (version 1.3.5)

Working examples for this chapter can be found at this GitHub link: https://github.com/PacktPublishing/Deep-Learning-with-PyTorch-Lightning/tree/main/Chapter05.

n order to make sure that these modules work together and not go out of sync, we have used the specific version of torch, torchvision, torchtext, torchaudio with PyTorch Lightning 1.5.2. You can also use the latest version of PyTorch Lightning and torch compatible with each other. More details can be found on the GitHub...

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