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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

Concepts

Author (1):

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

Video classification using Flash

Video classification is one of the most interesting yet challenging problems in DL. Simply speaking, it tries to classify an action in a video clip and recognize it (such as walking, bowling, or golfing):

Figure 4.1 – The Kinetics human action video dataset released by DeepMind is comprised of annotated ~10-second video clips sourced from YouTube

Training such a DL model is a challenging problem because of the sheer amount of compute power it takes to train the model, given the large size of video files compared to tabular or image data. Using a pre-trained model and architecture is a great way to start your experiments for video classification.

PyTorch Lightning Flash relies internally on the PyTorchVideo library for its backbone. PyTorchVideo caters to the ecosystem of video understanding. Lightning Flash makes it easy by creating the predefined and configurable hooks into the underlying framework. There are hooks...

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Authors (1)

Kunal Sawarkar

Kunal Sawarkar is a chief data scientist and AI thought leader. He leads the worldwide partner ecosystem in building innovative AI products. He also serves as an advisory board member and an angel investor. He holds a master's degree from Harvard University with major coursework in applied statistics. He has been applying machine learning to solve previously unsolved problems in industry and society, with a special focus on deep learning and self-supervised learning. Kunal has led various AI product R&D labs and has 20+ patents and papers published in this field. When not diving into data, he loves doing rock climbing and learning to fly aircraft, in addition to an insatiable curiosity for astronomy and wildlife.

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