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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

Summary

Time series modeling is one of the oldest and most ubiquitous applications of machine learning in industry and academia alike. In this chapter, we saw how PyTorch Lightning facilitates time series modeling using Deep Learning algorithms such as RNN and LSTM. This also provides data scientists with various tools to define RNN and LSTM configurations, with capabilities for easily monitoring learning and loss rates. We learned how we can quickly build complex Deep Learning models for time series forecasting using PyTorch Lightning.

We will continue our journey with PyTorch Lightning in the next chapter with one of the most fascinating and newest algorithms in machine learning – Generative Adversarial Networks (GANs). It can generate realistic faces of humans and objects that do not exist, and they look so real that no one can say for certain that they are artificial entities. Using PyTorch Lightning, you will see that it is much easier to carry out Generative Modeling...

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