Hands-On Graph Neural Networks Using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch (Paperback)

Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and apps

Purchase of the print or Kindle book includes a free PDF eBook

Key Features:

• Implement state-of-the-art graph neural network architectures in Python
• Create your own graph datasets from tabular data
• Build powerful traffic forecasting, recommender systems, and anomaly detection applications

Book Description:

Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery.

Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you'll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps.

By the end of this book, you'll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more.

What You Will Learn:

• Understand the fundamental concepts of graph neural networks
• Implement graph neural networks using Python and PyTorch Geometric
• Classify nodes, graphs, and edges using millions of samples
• Predict and generate realistic graph topologies
• Combine heterogeneous sources to improve performance
• Forecast future events using topological information
• Apply graph neural networks to solve real-world problems

Who this book is for:

This book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as students looking for a comprehensive reference on this rapidly growing field. Whether you're new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help you get the most out of this book.

使用PyTorch Geometric設計強大的圖形神經網絡，結合圖論和神經網絡的最新發展和應用。

購買印刷版或Kindle電子書，即可獲得免費的PDF電子書。

主要特點：
- 使用Python實現最先進的圖形神經網絡架構
- 從表格數據創建自己的圖形數據集
- 構建強大的交通預測、推薦系統和異常檢測應用

書籍描述：
圖形神經網絡是一種高效的數據分析工具，可用於分析可以表示為圖形的數據，例如社交網絡、化學化合物或運輸網絡。過去幾年中，圖形神經網絡的應用急劇增加，從自然語言處理和計算機視覺到推薦系統和藥物發現。

《使用Python進行圖形神經網絡實踐》從圖論的基礎知識開始，並向您展示如何從表格數據創建圖形數據集。隨著您的進一步學習，您將探索主要的圖形神經網絡架構，並學習圖形卷積、自注意力、鏈接預測和異構圖等基本概念。最後，本書提出了解決實際問題的應用，使您能夠建立專業的作品集。代碼已在線上提供，並且可以輕鬆適應其他數據集和應用。

通過閱讀本書，您將學習創建圖形數據集，使用Python和PyTorch Geometric實現圖形神經網絡，並應用它們解決現實世界的問題，包括構建和訓練用於節點和圖形分類、鏈接預測等的圖形神經網絡模型。

學習內容：
- 理解圖形神經網絡的基本概念
- 使用Python和PyTorch Geometric實現圖形神經網絡
- 使用數百萬個樣本對節點、圖形和邊進行分類
- 預測和生成真實的圖形拓撲
- 結合異構源以提高性能
- 使用拓撲信息預測未來事件
- 應用圖形神經網絡解決實際問題

本書適合機器學習從業人員和數據科學家，他們有興趣了解圖形神經網絡及其應用，以及尋找關於這個快速發展領域的全面參考資料的學生。無論您是新手還是希望提升知識水平，本書都能滿足您的需求。具備機器學習和Python編程的基礎知識將有助於您充分利用本書的內容。