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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

Product type Paperback

Published in May 2024

Publisher Packt

ISBN-13 9781801074308

Length 558 pages

Edition 2nd Edition

Tools

PyTorch

Concepts

Deep Learning

Author (1):

Ashish Ranjan Jha

View More author details

Table of Contents (21) Chapters

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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Authors (1)

Ashish Ranjan Jha

Ashish Ranjan Jha

Ashish Ranjan Jha received his bachelor's degree in electrical engineering from IIT Roorkee (India), a master's degree in Computer Science from EPFL (Switzerland), and an MBA degree from Quantic School of Business (Washington). He has received a distinction in all 3 of his degrees. He has worked for large technology companies, including Oracle and Sony as well as the more recent tech unicorns such as Revolut, mostly focused on artificial intelligence. He currently works as a machine learning engineer. Ashish has worked on a range of products and projects, from developing an app that uses sensor data to predict the mode of transport to detecting fraud in car damage insurance claims. Besides being an author, machine learning engineer, and data scientist, he also blogs frequently on his personal blog site about the latest research and engineering topics around machine learning.

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