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 Matrix Operations Practice Book for AI Learners : 20 Chapters of Worked Problems from Matrices and Eigenvalues to SVD, Backpropagation, and Transformers English | May 11, 2026 | ASIN: B0GYFVDRB3 | 457 pages | PDF | 43.98 MB Matrix Operations Practice Book for AI Learners is a structured, problem based workbook for students and practitioners who want to build a strong mathematical foundation for machine learning. Starting from the basics of reading and writing matrices, this book develops every core topic used in modern AI systems through clear explanations, worked examples, fill in the blank exercises, and unsolved practice problems.Across 20 chapters, readers work through matrices, vectors, dot products, matrix multiplication, transpose and inverse, determinants, eigenvalues, linear transformations, systems of equations, vector spaces, orthogonality, SVD, probability, gradients, convolutions, attention mechanisms, optimisation, regularization, dimensionality reduction, and full network mathematics. Every chapter includes an AI Connection section that shows directly how the concept is used in real machine learning systems.This workbook covers:Reading, writing, and operating on matrices and vectors.Eigenvalues, eigenvectors, and singular value decomposition.Gradients, backpropagation, and the chain rule.Convolutions, tensors, and attention mechanisms.Optimisation methods including SGD, momentum, and Adam.Regularization, PCA, t-SNE, and full network mathematics.This book is ideal for machine learning students, data scientists, AI engineers, and anyone who wants a clear, structured, and practical guide to the matrix mathematics behind modern neural networks.  Matrisensus: Masculine Collapse and Feminine Shadow English | February 3, 2026 | ASIN: B0GLB37H8X | 516 pages | Epub | 1.34 MB Public life now runs on outrage, accusation, and moral escalation. Speech is policed, standards are contested, and conflicts are framed through harm and victimhood. Matrisensus argues that these developments stem from a single, largely unrecognized transformation in our moral culture.Why has political disagreement become so hostile and absolute?Why do online debates escalate instantly into denunciation and public shaming?Why is free speech increasingly framed as harm?Why do institutions respond to conflict by managing feelings rather than enforcing standards?Why are social disputes so often cast in rigid victim-and-perpetrator narratives?Rather than treating these developments as separate problems-in politics, media, education, law, or social movements-Matrisensus argues that they arise from a single underlying shift in how moral authority now operates.As traditional expectations of responsibility, restraint, and earned authority weakened after the 1960s, they were gradually replaced by a new moral logic centered on emotional harm, identity, and protection. Disagreement became injury. Debate became aggression. Judgment became cruelty. Standards gave way to sensitivities; obligation gave way to grievance; authority gave way to moral signaling.Drawing on history, psychology, myth, and lived experience, the book traces how this shift first reshaped family life and education, then expanded into courts, universities, workplaces, healthcare, and public discourse. Practices originally intended to reduce injustice hardened into systems that reward victim status, discourage competence, and treat boundaries as oppression. Institutions increasingly struggle to distinguish fairness from favoritism, care from control, and protection from coercion.This is not an attack on compassion, nor a defense of past abuses of power. It is an attempt to describe-plainly and without ideology-what happens when the moral habits suited to family life are scaled up to govern entire societies, and when authority is expected to nurture rather than judge, protect rather than demand, and affirm rather than distinguish.Written for readers who sense that something has gone wrong but are dissatisfied with partisan explanations, Matrisensus provides a framework for understanding why polarization deepens, speech narrows, conflicts intensify, and trust erodes-and why restoring cultural balance requires recovering responsibility, reciprocity, and limits alongside care.  Mathematics in Deep Learning: CNNs, Transformers, Diffusion Models, and LLMs (Foundation Books) English | May 15, 2026 | ASIN: B0GX32M8SP | 548 pages | PDF | 91.92 MB Mathematics in Deep Learning is a practical textbook for readers who have seen neural networks in code and want the mathematics behind them to feel usable, explanatory, and connected to practice.The book builds the habits that make deep learning easier to reason аbout: tracking tensor shapes, reading models as parameterized functions, connecting losses to data, understanding optimization, and checking whether learned rules will behave well away from the training examples.Topics move from foundations to modern systems, including tensors, probability, empirical risk, convolution, backpropagation, CNN architecture, transfer learning, embeddings, sequence models, attention, transformers, autoregressive modeling, reinforcement learning, preference optimization, training theory, vision-language models, object detection, segmentation, generative modeling, speech recognition, and speech generation.Each chapter uses running examples, compact derivations, figures, exercises, and companion SymPy or numerical code to keep the mathematics tied to inspectable computations.For students, engineers, and self-study readers who want to understand deep learning models more clearly, not just run them.  Mathematics for artificial Intelligence-ii: (statistics and optimization) (Data Science) English | August 21, 2025 | ASIN: B0FN7FDG86 | 471 pages | Pdf | 5 MB Artificial Intelligence (AI) is no longer a futuristic dream; it has become the backbone of today's digital society, influencing everything from healthcare and finance to robotics, autonomous vehicles, and natural language processing. At the heart of every intelligent algorithm lies one common foundation: mathematics. While Volume I of this series established the fundamentals of linear algebra and probability, and your separate book on Statistics for Data Science covered the practical essentials, this Volume II is designed to take the learner deeper into the advanced statistical methods and optimization techniques that truly power state-of-the-art AI models.If Volume I was about building the walls of the AI temple, Volume II is about reinforcing its pillars and giving it strength to stand against the complexity of modern challenges. This book is not just about formulas and theorems-it is about understanding the mathematical reasoning that makes artificial intelligence systems reliable, efficient, and robust.Today's deep learning models with billions of parameters, reinforcement learning agents capable of defeating world champions, and generative AI systems that create human-like text, images, and music-all owe their success to statistics and optimization. Without advanced statistical methods, we cannot understand uncertainty, reliability, or the generalization capacity of a model. Without optimization, we cannot train networks, tune hyperparameters, or find efficient solutions to real-world problems.This book is a roadmap designed for students, researchers, data scientists, engineers, and AI enthusiasts who are eager to push beyond the basics and understand the mathematics that makes modern AI possible.Structure of the BookThis volume is divided into five parts with sixteen chapters, carefully structured to cover advanced statistics and optimization for artificial intelligence.Part I - Advanced Statistical Foundations for AIHere, we explore the statistical decision theories, multivariate statistics, and resampling methods that go beyond descriptive statistics. Readers will learn about covariance structures, multivariate normal distributions, factor analysis, and the crucial bias-variance trade-off in AI systems.Part II - Optimization for Artificial IntelligenceThis part introduces optimization in its purest form. Starting with the fundamentals of convex and non-convex optimization, it explores gradient-based optimization methods such as Adam, RMSProp, and NAG. It also covers convex optimization duality and real-world applications like Support Vector Machines (SVMs).Part III - Probabilistic and Information-Theoretic OptimizationModern AI does not rely only on deterministic optimization. This part focuses on probabilistic approaches like Monte Carlo methods, Bayesian optimization, and Expectation-Maximization. It also dives into information theory concepts such as entropy, KL divergence, and mutual information-tools critical in deep learning and generative models.Part IV - Advanced AI ApplicationsHere, the focus is on practical applications: optimization in neural networks, reinforcement learning, and hyperparameter tuning. Readers learn how to address problems like vanishing gradients, saddle points, and overfitting in real-world training.Part V - Practical Implementations and Case StudiesThis part brings everything together through coding examples and case studies. Using Python libraries like NumPy, CVXPY, TensorFlow, and PyTorch, readers can directly implement optimization techniques and apply them to computer vision, NLP, reinforcement learning, and healthcare applications.  Mathematics for AI, Machine Learning, and LLMs Made Easy: A Practical Guide to AI Mathematics, Machine Learning, Neural Networks, Transformers, and LLMs English | June 17, 2026 | ASIN: B0H5RD6F79 | 550 Pages | Epub | 593.50 KB 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.  Mathematics Year 9: Through The Tang Dinasty (Mathematics Through History) English | May 16, 2026 | ASIN: B0H1Z4PQHK | 209 pages | Epub | 27.30 MB Mathematics Year 9: Through the Lens of the Tang Dynasty is a unique mathematics textbook that combines modern secondary mathematics with the rich history, science, and culture of one of the world's greatest civilisations.Aligned with the New Zealand Curriculum (Level 5 / Year 9) and broadly compatible with international Year 9 programmes, this book provides comprehensive coverage of Number, Algebra, Geometry, Measurement, and Statistics through real historical contexts drawn from Tang Dynasty China (618-907 CE).Rather than presenting mathematics as a collection of abstract rules to memorise, this book shows students how mathematical ideas emerged from real human needs: designing palaces, surveying land, calculating taxes, managing trade along the Silk Road, constructing canals, tracking celestial movements, and governing one of the largest empires in world history.Students will encounter:Linear equations through Silk Road commerce;Geometry through Tang architecture and pagodas;Measurement through ancient Chinese engineering;Statistics through census and taxation records;And the Gougu theorem (Pythagoras) through historical Chinese mathematics.The Tang Dynasty was a golden age of scientific and mathematical achievement. During this period, Chang'an became the largest and most cosmopolitan city on Earth, the Imperial Examination System placed mathematical reasoning at the centre of government administration, and Chinese mathematicians worked with concepts such as negative numbers, fractions, simultaneous equations, and highly accurate approximations of π centuries before many of these ideas reached Europe.This book is the first volume in the Mathematics Through History series, which explores mathematics through different historical civilisations across Years 7-13, including Ancient Rome, the Islamic Golden Age, Renaissance Italy, and Ancient Greece.Features include:Full curriculum coverage for Year 9 mathematics;Clear worked examples and step-by-step explanations;Hundreds of exercises and review problems;Historical narratives integrated into every chapter;Extension and enrichment tasks;Full mock examinations for end-of-year preparation;Rich visual diagrams and historical illustrations.Designed for students, teachers, homeschoolers, and anyone interested in the history of mathematics, this book transforms mathematics from a disconnected school subject into a living human story.Because mathematics and history are not separate subjects - they are two ways of understanding the same civilisation.  Mathematical Foundations of AI VOL 1. : Neural Networks, Deep Learning, Transformers, Foundation Models, Agents, and the Geometry of Machine Thought (Artificial Intelligence) English | June 5, 2026 | ASIN: B0H45XFYFB | 1362 pages | Epub | 33.51 MB Complete mathematical descrption of historical Artificial Intelligence (AI) models proceeding to comtemporary Models. The books initiates with simple and ingenous algorythms to advanced mathematical models that are the structure of advanced AI models.  Math You Actually Need: A Practical Guide for Programmers English | March 29, 2026 | ASIN: B0GHZC31KN | 505 pages | Epub | 13.25 MB Your loop runs in 12 seconds. A single formula drops it to 0.003.This book shows you the exact math that makes code faster - no proofs, no chalkboard lectures, no "you'll need this someday." Every chapter starts with a real programming problem, solves it the obvious (slow) way, then reveals the mathematical insight that makes it 10x to 1,000,000x faster. Every claim is backed by a timed benchmark you can run yourself.What you'll learn:Why your nested loop is secretly O(n²) - and the closed-form formula that replaces it (Chapter 1: Arithmetic Series)How binary search cuts a million-item lookup from 1,000,000 steps to 20 (Chapter 2: Logarithms)The trick that lets databases, caches, and load balancers wrap around cleanly - modular arithmetic (Chapter 4)How to pack four values into a single integer using bitwise operations (Chapter 5)Why password strength is a math problem, not a length problem (Chapter 6: Combinatorics)How to estimate unique visitors in 12 KB of memory instead of gigabytes (Chapter 7: Probability & HyperLogLog)The set theory behind SQL JOINs - and why knowing it makes you write better queries (Chapter 9)How Dijkstra's algorithm finds the shortest path in a network (Chapter 11: Graph Theory)The linear algebra that powers image filters, 3D games, and recommendation engines (Chapter 13)How RSA encryption works - and why factoring large numbers keeps it secure (Chapter 14: Number Theory)Each of the 16 chapters follows the same structure:A real programming problemThe naive (slow) solution - working code you can runThe math insight, explained with intuition - not formulasThe better solution - working code you can runA benchmark proving the differenceAll 130 Python code examples are runnable as-is and available in a companion GitHub repository. The concepts are language-agnostic - if you code in jаvascript, Go, Rust, or Java, the math works the same way.This book is for working programmers, self-taught developers, bootcamp graduates, and anyone preparing for technical interviews. No math degree required. If you can write a for-loop, you can read this book.You don't need more math. You need the right math.  Math Workbook Grade 7-8 : Integers, Algebra, Ratios, Geometry, Statistics and Word Problems English | June 13, 2026 | ASIN: B0H51CLJ2V | 248 pages | Epub | 543.10 KB Is your middle schooler struggling to keep up in math - or are you looking for a workbook that actually covers everything in one place? This comprehensive Math Workbook for Grades 7-8 gives students 256 pages of structured, curriculum-aligned practice designed to build confidence and mastery from the ground up.Written for students ages 12 to 14, this workbook bridges 7th grade math and pre-algebra, covering every major topic students need before entering 8th grade and Algebra 1. Whether you are a parent supporting learning at home, a homeschool educator building a complete curriculum, or a classroom teacher looking for supplemental practice, this workbook delivers everything in one organized resource.What is inside this workbook:13 complete units covering Integers and Rational Numbers, Fractions, Decimals and Percents, Ratios and Proportional Relationships, Algebra Expressions, Equations and Inequalities, Linear Relationships and Functions, Geometry with Angles and Transformations, Area, Surface Area and Volume, the Pythagorean Theorem, Statistics and Data Analysis, Probability, Exponents and Scientific Notation, and Mixed Multi-Step Word Problems.Each lesson includes a clear Key Concept box, a worked example, and graded practice problems. Every unit ends with a Review and Challenge section. The book also includes a full Cumulative Review Test, a complete Answer Key with step-by-step explanations, a Math Vocabulary Glossary, a Formula Reference Sheet, and blank Notes pages for student workspace.Key features:Aligned to Common Core State Standards for Grades 7 and 8256 pages of structured practice problems and word problemsFull Answer Key with explanations includedIdeal for homeschool, classroom, tutoring, and independent studySkills Progress Tracker to monitor student advancementReal-world word problems in every unitFormula Reference Sheet students can use as a study guideSuitable for 7th graders, 8th graders, and students preparing for pre-algebraThis is the complete, no-gaps middle school math companion that parents and teachers have been searching for. Get your student on the path to pre-algebra mastery today.  Mastering the Model Context Protocol (MCP): Standardizing Tool Access, Context, and Permissions for AI Agents English | 21 May 2026 | ASIN: B0H1YKG8BY | 303 pages | EPUB (True) | 8.84 MB "Mastering the Model Context Protocol (MCP): Standardizing Tool Access, Context, and Permissions for AI Agents"As AI agents move from demos to production systems, the hardest problems are no longer just model quality-they are interoperability, permissions, context handling, and safe access to external tools. This book is written for experienced engineers, architects, platform teams, and advanced AI practitioners who need a rigorous understanding of the Model Context Protocol (MCP) as an emerging standard for connecting models to real-world systems.You will learn how MCP structures host-client-server interactions, how its core primitives-tools, resources, prompts, and sampling-shape reliable agent behavior, and how transport, session, schema, and validation choices affect interoperability across implementations. The book also goes deep on security-first design, OAuth-based authorization for remote servers, production-grade server and client architecture, observability, testing, compatibility strategy, and multi-server workflow composition. By the end, readers will be able to design, evaluate, and operate MCP-based systems with technical precision and sound judgment.Rather than offering a superficial tour, this book treats MCP as both a protocol and an architectural discipline. It assumes comfort with distributed systems, APIs, JSON-based contracts, and modern AI application design, and it distinguishes itself through its emphasis on trade-offs, failure modes, and long-term maintainability in fast-evolving agent ecosystems. |
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