Book Image

Mastering PyTorch - Second Edition

By : Ashish Ranjan Jha
4 (1)
Book Image

Mastering PyTorch - Second Edition

4 (1)
By: Ashish Ranjan Jha

Overview of this book

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
Table of Contents (21 chapters)
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.