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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
Tools
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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

Building PyTorch apps on iOS

In this section, we will create an iOS app that allows you to perform handwritten digit classification on camera-captured images of handwritten digits. We will reuse the mobile-optimized MNIST model from the Android app development section. All code for this section is available on GitHub [11].

Setting up the iOS development environment

To build iOS apps, you need to download Xcode [12] on your MacBook. Xcode is an IDE created by Apple for developing software on iOS (and also on macOS, iPadOS, watchOS, and tvOS). It is the primary tool used by developers to create applications and software for Apple’s various platforms.

Note

You will need Xcode version 11 or higher to build this app. Xcode is the IDE to build iOS apps. You must have a MacBook to build iOS apps.

Before we open Xcode, from the command line, we need to first set the current working directory to the iOS/HelloWorld folder [13]. From this directory, we...

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