Machine Sees Pattern Through Math (Paperback)

Stalin Subramani

ISBN 13: 9798901766019

Published by Notion Press, 2025

About this Item

Paperback. Ever wondered how machines actually think?Behind every prediction, every smart assistant, and every AI decision lies the invisible language of mathematics. But here's the truth - you don't need to be a math genius to understand it. What you really need is intuition.This book takes you on a journey to uncover how math shapes machine learning - not through complex formulas, but through simple, real-world insights. You'll see how numbers become the building blocks of intelligence, how vector spaces help machines recognize patterns, and how transformations let them "zoom in" to find meaning in chaos.You'll explore why determinants tell us how spaces stretch and shrink, how probability gives machines the power to learn from uncertainty, and how calculus helps them correct their own mistakes - just like we do.From playful stories (including one inspired by a game with the author's kids) to hands-on examples and code, this book makes the abstract concrete. By the end, you'll not only understand concepts like linear regression, logistic regression, and neural networks - you'll feel how the math behind them actually works.If you've ever wanted to truly understand how machines learn - this is where your journey begins. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9798901766019

Bibliographic Details

• Title: Machine Sees Pattern Through Math (Paperback)
• Publisher: Notion Press
• Publication Date: 2025
• Language: English
• ISBN 13: 9798901766019
• Binding: Paperback
• Condition: new

About this title

Synopsis

Ever wondered how machines actually think?

Behind every prediction, every smart assistant, and every AI decision lies the invisible language of mathematics. But here’s the truth — you don’t need to be a math genius to understand it. What you really need is intuition.

This book takes you on a journey to uncover how math shapes machine learning — not through complex formulas, but through simple, real-world insights. You’ll see how numbers become the building blocks of intelligence, how vector spaces help machines recognize patterns, and how transformations let them “zoom in” to find meaning in chaos.

You’ll explore why determinants tell us how spaces stretch and shrink, how probability gives machines the power to learn from uncertainty, and how calculus helps them correct their own mistakes — just like we do.

From playful stories (including one inspired by a game with the author’s kids) to hands-on examples and code, this book makes the abstract concrete. By the end, you’ll not only understand concepts like linear regression, logistic regression, and neural networks — you’ll feel how the math behind them actually works.

If you’ve ever wanted to truly understand how machines learn — this is where your journey begins.