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Deep Learning with PyTorch Lightning

You're reading from  Deep Learning with PyTorch Lightning

Product type Book
Published in Apr 2022
Publisher Packt
ISBN-13 9781800561618
Pages 366 pages
Edition 1st Edition
Languages
Author (1):
Kunal Sawarkar Kunal Sawarkar
Profile icon Kunal Sawarkar

Table of Contents (15) Chapters

Preface 1. Section 1: Kickstarting with PyTorch Lightning
2. Chapter 1: PyTorch Lightning Adventure 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 and with Python 3.6. If you are using another environment, please make 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 1.5.10)
  • Flash (version 0.7.1)
  • 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/Chapter04.

The source datasets can be found at the Kinetics 400 dataset source: https://deepmind.com/research/open-source/kinetics.

This is the video classification dataset that has been created by DeepMind by scraping YouTube videos. The kinetics dataset was made available by Google Inc. and is one...

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