Analytical Geometry for Machine Learning : Vectors, Transformations, and Spatial Modeling in Applied AI

Hayden van der Post

ISBN 13: 9798252518213

Published by Independently Published Mär 2026, 2026

About this Item

Neuware.

Seller Inventory # 9798252518213

Bibliographic Details

• Title: Analytical Geometry for Machine Learning : Vectors, Transformations, and Spatial Modeling in Applied AI
• Publisher: Independently Published Mär 2026
• Publication Date: 2026
• Language: English
• ISBN 13: 9798252518213
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 32D millimeters 229 millimeters width by 152 millimeters height by 32 millimeters depth
• Weight: 601 grams

About this title

Synopsis

Reactive Publishing

Analytical geometry forms the mathematical foundation behind modern machine learning systems, enabling models to interpret structure, distance, and transformation in high-dimensional space.

This book presents a structured approach to analytical geometry with a focus on its role in machine learning and applied AI. It develops the core concepts required to understand how vectors, coordinate systems, and geometric transformations operate within data-driven models.

Topics include vector operations, linear transformations, coordinate mappings, and spatial representations used in machine learning workflows. Each concept is explored with practical context, connecting geometric intuition to real-world applications such as feature spaces, embeddings, and model optimization.

Designed for readers with a basic background in mathematics, this book bridges the gap between classical geometry and modern computational systems. It provides a clear framework for understanding how spatial reasoning underpins many of the techniques used in machine learning today.