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Mastering PyTorch

You're reading from   Mastering PyTorch Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781801074308
Length 558 pages
Edition 2nd Edition
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Author (1):
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Ashish Ranjan Jha Ashish Ranjan Jha
Author Profile Icon Ashish Ranjan Jha
Ashish Ranjan Jha
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Table of Contents (21) Chapters Close

Preface 1. Overview of Deep Learning Using PyTorch 2. Deep CNN Architectures FREE CHAPTER 3. Combining CNNs and LSTMs 4. Deep Recurrent Model Architectures 5. Advanced Hybrid Models 6. Graph Neural Networks 7. Music and Text Generation with PyTorch 8. Neural Style Transfer 9. Deep Convolutional GANs 10. Image Generation Using Diffusion 11. Deep Reinforcement Learning 12. Model Training Optimizations 13. Operationalizing PyTorch Models into Production 14. PyTorch on Mobile Devices 15. Rapid Prototyping with PyTorch 16. PyTorch and AutoML 17. PyTorch and Explainable AI 18. Recommendation Systems with PyTorch 19. PyTorch and Hugging Face 20. Index

Summary

In this chapter, we first learned about PyTorch Mobile and how it can be used to convert traced PyTorch model artifacts into optimized model objects that can run on mobile devices. We then learned how to build an Android app that uses PyTorch Mobile to classify images of handwritten digit captured by a phone camera using a pre-trained MNIST model. We then repeated this exercise for iOS, where we built an iOS app, again from scratch, to classify images of handwritten digits into one of 10 classes. In the next chapter, we will discuss various tools and libraries such as fastai and PyTorch Lightning that speed up and simplify the process of model training in PyTorch. We will also learn how to profile PyTorch code to understand resource utilization, using PyTorch profiler.

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