Mathematics Machine Learning Llms by Liew Voon (4 results)
- Softcover
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
Contact seller5-star sellerCondition: New
£ 31.54
£ 5.87 shippingShips from United Kingdom to U.S.A.Quantity: Over 20 available
PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
Contact seller5-star sellerCondition: New
£ 50.17
£ 55.05 shippingShips from Germany to U.S.A.Quantity: 2 available
Taschenbuch. Condition: Neu. 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.
- Softcover
- Print on Demand
Seller: California Books, Miami, FL, U.S.A.California Books
Contact seller4-star sellerCondition: New
£ 32.49
Free ShippingShips within U.S.A.Quantity: Over 20 available
Condition: New. Print on Demand.
- Softcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
Contact seller5-star sellerCondition: New
£ 34.99
£ 37.00 shippingShips from United Kingdom to U.S.A.Quantity: 1 available
Paperback. Condition: new. Paperback. 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, m…achine 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
