Mathematics for AI, Machine Learning, and LLMs Made Easy : A Practical Guide to AI Mathematics, Machine Learning, Neural Networks, Transformers, and LLMs

Voon Kiong Liew

ISBN 13: 9798181999756

Published by Independently Published Jun 2026, 2026

About this Item

Neuware - Artificial intelligence may look complex, but behind every AI model are mathematical ideas that can be understood step by step.Mathematics for AI, Machine Learning, and LLMs Made Easy is a practical beginner-friendly guide to the essential mathematics behind modern artificial intelligence, machine learning, deep learning, embeddings, transformers, and large language models.Written in a clear 'Made Easy' style, this book explains important concepts such as vectors, matrices, dot products, similarity, distance, calculus, gradients, loss functions, gradient descent, backpropagation, probability, statistics, Bayes' Theorem, regression, classification, clustering, neural networks, embeddings, attention mechanisms, transformers, and LLMs.This book is designed for students, developers, educators, business professionals, AI enthusiasts, and anyone who wants to understand how AI works without being overwhelmed by advanced mathematical notation.Inside this book, you will learn: How data is represented using vectors, matrices, and feature spacesWhy dot products, similarity, and distance are important in AIHow calculus, derivatives, and gradients help models learnHow loss functions and gradient descent train machine learning modelsHow probability and statistics support prediction, uncertainty, and evaluationHow regression, classification, and clustering workHow neural networks use weights, biases, layers, and activation functionsHow embeddings turn words, documents, images, users, and products into vectorsHow attention and self-attention power transformer modelsHow large language models predict and generate textHow mathematics is applied in real-world AI, RAG systems, recommendation engines, AI agents, fraud detection, forecasting, search, and business applicationsEach chapter explains the concepts in simple language with practical examples, formulas, review questions, and exercises to help reinforce learning.Whether you are preparing to study machine learning, building AI applications, exploring large language models, or trying to understand the mathematics behind modern AI tools, this book gives you a strong and practical foundation.If you want to understand AI beyond the buzzwords, this book will help you see that AI is not magic. It is mathematics, data, models, and careful system design working together.

Seller Inventory # 9798181999756

Bibliographic Details

• Title: Mathematics for AI, Machine Learning, and LLMs Made Easy : A Practical Guide to AI Mathematics, Machine Learning, Neural Networks, Transformers, and LLMs
• Publisher: Independently Published Jun 2026
• Publication Date: 2026
• Language: English
• ISBN 13: 9798181999756
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 47D millimeters 229 millimeters width by 152 millimeters height by 47 millimeters depth
• Weight: 883 grams

About this title

Synopsis

Artificial intelligence may look complex, but behind every AI model are mathematical ideas that can be understood step by step.

Mathematics for AI, Machine Learning, and LLMs Made Easy is a practical beginner-friendly guide to the essential mathematics behind modern artificial intelligence, machine learning, deep learning, embeddings, transformers, and large language models.

Written in a clear “Made Easy” style, this book explains important concepts such as vectors, matrices, dot products, similarity, distance, calculus, gradients, loss functions, gradient descent, backpropagation, probability, statistics, Bayes’ Theorem, regression, classification, clustering, neural networks, embeddings, attention mechanisms, transformers, and LLMs.

This book is designed for students, developers, educators, business professionals, AI enthusiasts, and anyone who wants to understand how AI works without being overwhelmed by advanced mathematical notation.

Inside this book, you will learn:

How data is represented using vectors, matrices, and feature spaces

Why dot products, similarity, and distance are important in AI

How calculus, derivatives, and gradients help models learn

How loss functions and gradient descent train machine learning models

How probability and statistics support prediction, uncertainty, and evaluation

How regression, classification, and clustering work

How neural networks use weights, biases, layers, and activation functions

How embeddings turn words, documents, images, users, and products into vectors

How attention and self-attention power transformer models

How large language models predict and generate text

How mathematics is applied in real-world AI, RAG systems, recommendation engines, AI agents, fraud detection, forecasting, search, and business applications

Each chapter explains the concepts in simple language with practical examples, formulas, review questions, and exercises to help reinforce learning.

Whether you are preparing to study machine learning, building AI applications, exploring large language models, or trying to understand the mathematics behind modern AI tools, this book gives you a strong and practical foundation.

If you want to understand AI beyond the buzzwords, this book will help you see that AI is not magic. It is mathematics, data, models, and careful system design working together.