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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 9: Deploying and Scoring Models

Without knowing it, you may have already experienced some of the models we have covered so far in this book. Recall how your photos app can automatically detect faces in your picture collections or group all your pictures with a particular friend together. That is nothing more than an image recognition Deep Learning model in action (the likes of Convolutional Neural Networks (CNNs)), or you might be familiar with Alexa listening to your voice or Google autocompleting your text while searching for a query. Those are NLP-based Deep Learning models making things easier for us. Or you might have seen some e-shopping apps or social media sites suggesting captions for a product; that is semi-supervised learning in its full glory! But how do you take a model that you have built in a Python Jupyter notebook and make it consumable on devices, be it a speaker, a phone, an app, or a portal? Without application integration, a trained model remains a statistical...

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