Introduction of Geometric Algebra for Machine Learning and Data Science : A Unified Geometric Framework for Modern Learning Systems
Language: English
Published by Self Publishing LLC, 2026
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 45.99
Quantity: 2 available
Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - What if machine learning could be understood geometrically This book presents a unified geometric perspective on machine learning, statistics, and data science through the language of geometric algebra.From linear models and principal component analysis to neural networks, attention mechanisms, and time series systems, modern methods are reinterpreted as geometric transformations in n-dimensional spaces.Rather than treating techniques as isolated tools, this book reveals the common structure underlying them: movement, orientation, and shape.¿ Connects machine learning methods through geometry ¿ Covers PCA, neural networks, attention, and time series ¿ Includes PyTorch implementations ¿ Bridges theory and real-world applications ¿ Emphasizes intuition over formalism.
Seller Inventory # 9798295882890
- Title
- Introduction of Geometric Algebra for Machine Learning and Data Science : A Unified Geometric Framework for Modern Learning Systems
- Author
- Sandi Setiawan
- Publisher
- Self Publishing LLC
- Publication year
- 2026
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 13
- 9798295882890
- Item weight
- 874 grams
- Dimensions
- 286x221x18 mm
What if machine learning could be understood geometrically?
This book presents a unified geometric perspective on machine learning, statistics, and data science through the language of geometric algebra.
From linear models and principal component analysis to neural networks, attention mechanisms, and time series systems, modern methods are reinterpreted as geometric transformations in n-dimensional spaces.
Rather than treating techniques as isolated tools, this book reveals the common structure underlying them: movement, orientation, and shape.
• Connects machine learning methods through geometry
• Covers PCA, neural networks, attention, and time series
• Includes PyTorch implementations
• Bridges theory and real-world applications
• Emphasizes intuition over formalism
"Synopsis" may belong to another edition of this title.
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