{"product_id":"mathematics-for-ai-machine-learning-voon-kiong-liew-9798181999756","title":"Mathematics for AI, Machine Learning, and LLMs Made Easy: A Practical Guide to AI Mathematics, Machine Learning, Neural Networks, Transformers, and LL","description":"\u003cp\u003eArtificial intelligence may look complex, but behind every AI model are mathematical ideas that can be understood step by step.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eMathematics for AI, Machine Learning, and LLMs Made Easy\u003c\/b\u003e is a practical beginner-friendly guide to the essential mathematics behind modern artificial intelligence, machine learning, deep learning, embeddings, transformers, and large language models.\u003c\/p\u003e\u003cp\u003eWritten 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.\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003cp\u003eInside this book, you will learn: \u003c\/p\u003e\u003cp\u003eHow data is represented using vectors, matrices, and feature spaces\u003c\/p\u003e\u003cp\u003eWhy dot products, similarity, and distance are important in AI\u003c\/p\u003e\u003cp\u003eHow calculus, derivatives, and gradients help models learn\u003c\/p\u003e\u003cp\u003eHow loss functions and gradient descent train machine learning models\u003c\/p\u003e\u003cp\u003eHow probability and statistics support prediction, uncertainty, and evaluation\u003c\/p\u003e\u003cp\u003eHow regression, classification, and clustering work\u003c\/p\u003e\u003cp\u003eHow neural networks use weights, biases, layers, and activation functions\u003c\/p\u003e\u003cp\u003eHow embeddings turn words, documents, images, users, and products into vectors\u003c\/p\u003e\u003cp\u003eHow attention and self-attention power transformer models\u003c\/p\u003e\u003cp\u003eHow large language models predict and generate text\u003c\/p\u003e\u003cp\u003eHow mathematics is applied in real-world AI, RAG systems, recommendation engines, AI agents, fraud detection, forecasting, search, and business applications\u003c\/p\u003e\u003cp\u003eEach chapter explains the concepts in simple language with practical examples, formulas, review questions, and exercises to help reinforce learning.\u003c\/p\u003e\u003cp\u003eWhether 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.\u003c\/p\u003e\u003cp\u003eIf 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.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Voon Kiong Liew\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798181999756\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 06\/17\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 744\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.94lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.00h x 6.00w x 1.84d","brand":"Voon Kiong Liew","offers":[{"title":"Paperback","offer_id":48997792514303,"sku":"9798181999756","price":39.9,"currency_code":"USD","in_stock":true}],"url":"https:\/\/www.whiterainbookhouse.com\/products\/mathematics-for-ai-machine-learning-voon-kiong-liew-9798181999756","provider":"WR Book House","version":"1.0","type":"link"}