Graph Neural Networks in Action
暫譯: 圖神經網絡實戰
Broadwater, Keita, Stillman, Namid
- 出版商: Manning
- 出版日期: 2025-04-15
- 售價: $2,210
- 貴賓價: 9.5 折 $2,100
- 語言: 英文
- 頁數: 392
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1617299057
- ISBN-13: 9781617299056
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商品描述
A hands-on guide to powerful graph-based deep learning models. In Graph Neural Networks in Action, you will learn how to:
Graph Neural Networks in Action teaches you to create powerful deep learning models for working with graph data. You'll learn how to both design and train your models, and how to develop them into practical applications you can deploy to production. Go hands-on and explore relevant real-world projects as you dive into graph neural networks perfect for node prediction, link prediction, and graph classification. Inside this practical guide, you'll explore common graph neural network architectures and cutting-edge libraries, all clearly illustrated with well-annotated Python code. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology
Graph neural networks expand the capabilities of deep learning beyond traditional tabular data, text, and images. This exciting new approach brings the amazing capabilities of deep learning to graph data structures, opening up new possibilities for everything from recommendation engines to pharmaceutical research. About the book
In Graph Neural Networks in Action you'll create deep learning models that are perfect for working with interconnected graph data. Start with a comprehensive introduction to graph data's unique properties. Then, dive straight into building real-world models, including GNNs that can generate node embeddings from a social network, recommend eCommerce products, and draw insights from social sites. This comprehensive guide contains coverage of the essential GNN libraries, including PyTorch Geometric, DeepGraph Library, and Alibaba's GraphScope for training at scale. About the reader
For Python programmers familiar with machine learning and the basics of deep learning. About the author
Keita Broadwater, PhD, MBA is a machine learning engineer with over ten years executing data science, analytics, and machine learning applications and projects. He is Chief of Machine Learning at candidates.ai, a firm which uses AI to enhance executive search. Dr. Broadwater has delivered DS and ML projects for all types of organizations, from small startups to Fortune 500 companies, and has developed and advised on graph-related projects in the industries of insurance, HR and recruiting, and supply chain.
- Train and deploy a graph neural network Generate node embeddings Use GNNs at scale for very large datasets Build a graph data pipeline Create a graph data schema Understand the taxonomy of GNNs Manipulate graph data with NetworkX
Graph Neural Networks in Action teaches you to create powerful deep learning models for working with graph data. You'll learn how to both design and train your models, and how to develop them into practical applications you can deploy to production. Go hands-on and explore relevant real-world projects as you dive into graph neural networks perfect for node prediction, link prediction, and graph classification. Inside this practical guide, you'll explore common graph neural network architectures and cutting-edge libraries, all clearly illustrated with well-annotated Python code. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology
Graph neural networks expand the capabilities of deep learning beyond traditional tabular data, text, and images. This exciting new approach brings the amazing capabilities of deep learning to graph data structures, opening up new possibilities for everything from recommendation engines to pharmaceutical research. About the book
In Graph Neural Networks in Action you'll create deep learning models that are perfect for working with interconnected graph data. Start with a comprehensive introduction to graph data's unique properties. Then, dive straight into building real-world models, including GNNs that can generate node embeddings from a social network, recommend eCommerce products, and draw insights from social sites. This comprehensive guide contains coverage of the essential GNN libraries, including PyTorch Geometric, DeepGraph Library, and Alibaba's GraphScope for training at scale. About the reader
For Python programmers familiar with machine learning and the basics of deep learning. About the author
Keita Broadwater, PhD, MBA is a machine learning engineer with over ten years executing data science, analytics, and machine learning applications and projects. He is Chief of Machine Learning at candidates.ai, a firm which uses AI to enhance executive search. Dr. Broadwater has delivered DS and ML projects for all types of organizations, from small startups to Fortune 500 companies, and has developed and advised on graph-related projects in the industries of insurance, HR and recruiting, and supply chain.
商品描述(中文翻譯)
實用的圖形基礎深度學習模型指南。
在圖形神經網絡實戰中，您將學習如何：- 訓練和部署圖形神經網絡 生成節點嵌入 在非常大的數據集上大規模使用 GNN 建立圖形數據管道 創建圖形數據架構 理解 GNN 的分類法 使用 NetworkX 操作圖形數據
圖形神經網絡實戰教您如何創建強大的深度學習模型以處理圖形數據。您將學習如何設計和訓練模型，以及如何將其開發成可部署到生產環境的實用應用。親自動手，探索相關的現實世界項目，深入了解適合節點預測、連結預測和圖形分類的圖形神經網絡。在這本實用指南中，您將探索常見的圖形神經網絡架構和尖端庫，所有內容都用清晰的註釋 Python 代碼進行說明。 購買印刷版書籍可獲得 Manning Publications 提供的免費 PDF、Kindle 和 ePub 格式電子書。 關於技術
圖形神經網絡擴展了深度學習的能力，超越了傳統的表格數據、文本和圖像。這種令人興奮的新方法將深度學習的驚人能力帶入圖形數據結構，為從推薦引擎到製藥研究等各種應用開啟了新的可能性。 關於本書
在圖形神經網絡實戰中，您將創建適合處理互聯圖形數據的深度學習模型。首先，全面介紹圖形數據的獨特屬性。然後，直接進入構建現實世界模型，包括能夠從社交網絡生成節點嵌入、推薦電子商務產品和從社交網站獲取見解的 GNN。這本全面的指南涵蓋了重要的 GNN 庫，包括 PyTorch Geometric、DeepGraph Library 和阿里巴巴的 GraphScope，以便進行大規模訓練。 關於讀者
適合熟悉機器學習和深度學習基礎的 Python 程式設計師。 關於作者
Keita Broadwater，博士，MBA，是一位擁有十多年數據科學、分析和機器學習應用及項目經驗的機器學習工程師。他是 candidates.ai 的機器學習主管，該公司利用 AI 來增強高管搜尋。Broadwater 博士為各類組織提供了數據科學和機器學習項目，從小型初創公司到《財富》500 強企業，並在保險、人力資源和招聘以及供應鏈等行業開發和建議了與圖形相關的項目。
作者簡介
Keita Broadwater, PhD, MBA is a machine learning engineer with over ten years executing data science, analytics, and machine learning applications and projects. He is Chief of Machine Learning at candidates.ai, a firm which uses AI to enhance executive search. Dr. Broadwater has delivered DS and ML projects for all types of organizations, from small startups to Fortune 500 companies, and has developed and advised on graph-related projects in the industries of insurance, HR and recruiting, and supply chain. Namid Stillman, PhD is a research scientist and machine learning engineer with more than 20 peer-reviewed publications.
作者簡介(中文翻譯)
Keita Broadwater，博士，MBA，是一位擁有超過十年執行數據科學、分析和機器學習應用及專案的機器學習工程師。他是 candidates.ai 的機器學習主管，該公司利用人工智慧來提升高管搜尋的效率。Broadwater 博士為各類型的組織提供數據科學和機器學習專案，從小型初創公司到《財富》500 強企業，並在保險、人力資源與招聘以及供應鏈等行業開發和提供與圖形相關的專案建議。
Namid Stillman，博士，是一位研究科學家和機器學習工程師，擁有超過 20 篇經過同行評審的出版物。
