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

Introduction to GNNs

To understand GNNs, we will start with reviewing the graph data structure. That said, so what is a graph? In computer science, it refers to a data structure containing two components: nodes (or vertices) and edges, where nodes typically represent things or objects, such as a person, place, or thing, and edges connect the nodes, for example, by representing the relationship between the nodes. Figure 6.1 shows a graph with persons as nodes and their relationships as edges. Note that the edges are drawn as arrows, which indicates that this is a directed graph wherein there is a strict order in relationships (B is the parent of A, not the other way round). Undirected graphs, on the other hand, have edges that can be traversed both ways (think of the sibling relationship) and are drawn as straight lines between nodes.

Figure 6.1: Example graph containing people (nodes) and their relationships (edges)

In technical terms, a graph can be represented as

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