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Gears Don't Guess: The Executive's Practical Guide to Thriving in the Face of AI Hype and Risk
English | September 24, 2026 | ASIN: B0HKVG95NY | 344 pages | Epub | 744.79 KB
Right now you're doing one of two things with AI. Saying no to everything while your competitors pull ahead. Or saying yes and hoping nothing important breaks. Both are guesses."Most AI security advice today is just wishful thinking wrapped in a system prompt. Kip Boyle brings us back to reality." (Dr. Anton Chuvakin, Google Cloud Office of the CISO)"Board members will find Kip's book especially useful because it turns a technical topic into a risk-management discussion that helps us perform our job of oversight." (Dr. Keri Pearlson, MIT Sloan School)You've governed technology for years. Budgets, vendors, compliance, cybersecurity. You set the rules and walk away. AI feels different, and you probably can't say why. Here's why: it's the first system you've ever been handed that guesses back. The same question gets a different answer on Tuesday than it did Monday. And the answer often looks right even when it's wrong.This is a practical operating model for the leader who buys AI and answers for it, not the engineer who builds it. It's AI governance in plain English: no code, no promise that you can make AI perfectly safe, just the risk-based thinking you already use, pointed at a machine that guesses instead of calculates.Inside, you'll learn how to:Find the AI already in your stack that nobody approvedDecide what's allowed: acceptable use, data boundaries, and access tiersOwn AI risk and report it to your board without "committee theater"Roll out AI governance in your first 90 days, sized to your organizationRespond when AI breaks, and know what's different from a cyber incidentSecurity isn't the roadblock to AI adoption. It's what makes adoption safe enough to speed up. The companies that win won't be the ones that move fastest. They'll be the ones that govern AI well enough to keep going when something breaks.

Galois Groups and Field Extensions for Solvable Quintics: Analysis of irreducible and reducible polynomials of degree 5
English | June 2, 2022 | ASIN: B0B31SLF19 | 50 pages | EPUB (True) | 7.66 MB
In the context of self-studies on polynomials of degree 5 (quintics), Galois groups and field extensions related to solvable quintics - irreducible and reducible ones - have been analyzed. Effort has been spent to provide comprehensive explanations and some examples of those structures; the intention is to give the reader an overview and a decent insight into the topic, which may help to understand the algebraic structures and their arithmetics better.

GPU Guide for Local LLMs: Hardware, Cost, and Performance Tradeoffs Explained
English | September 18, 2026 | ASIN: B0HFK15NMD | 196 pages | EPUB (True) | 1.64 MB
The moment you decide to run a large language model on your own hardware, you cross a threshold that most people never even approach. You move from being a passive consumer of AI services to an active participant who owns the technology outright. This shift carries profound implications for privacy, cost, and creative freedom, but it also introduces a problem that the cloud generation rarely thinks about. You now have to choose the hardware that will power your AI, and that choice will determine everything about your experience.Your choice of GPU will determine everything about your local LLM experience, from which models you can run to how fast they respond to how much your electricity bill increases each month. This book provides a comprehensive framework for understanding the tradeoffs between VRAM capacity, memory bandwidth, quantization levels, and total cost of ownership. You will learn how to evaluate hardware based on what you actually need to accomplish, not just what the spec sheet says.Inside, you'll discover:* Why VRAM capacity is the single most important specification for local LLMs* How quantization lets you run larger models on modest hardware* The NVIDIA advantage and when AMD or Apple Silicon makes sense* How to navigate the used and enterprise GPU market* Multi-GPU configurations and when they are worth the complexity* The hidden costs of running local AI including electricity and maintenance* Budget strategies for building your ideal local LLM systemThe local AI landscape is evolving faster than any hardware market you have ever seen.This book gives you the mental model to evaluate new hardware for years, regardless of which specific products come and go. Master these principles and make the right choice for your needs.

From Regression to Language Models: The Handbook of Machine Learning and Artificial Intelligence - Mathematics, Methods, Practice
English | October 10, 2026 | ASIN: B0HMKR6WYJ | 1768 pages | Epub | 28.02 MB
Machine learning has become a basic technology, from crop yield forecasts to language assistants. This handbook explains the methods of machine learning and artificial intelligence completely and according to a uniform scheme: How does a method work, including the mathematics it requires? What is it used for? What are its strengths and weaknesses, and where do they come from? When is it the right choice?The mathematics is not only derived but explained in words: every formula is motivated, every step is justified, and every result is interpreted, often with a small numerical example.Foundations: linear algebra, calculus, statistics, information and learning theory, evaluationClassical methods: regression, SVMs, decision trees, ensembles, Gaussian processes, clusteringDeep learning: CNNs, RNNs, transformers, graph neural networks, generative models, diffusionReinforcement learning, large language models, RAG, agents, and multimodal modelsTime series, recommender systems, causal learning, and symbolic AIExplainability, fairness, MLOps, law, and ethicsMore than 150 runnable Python examples with real outputs, decision guides in every chapter, and a profile of every method make the book both a textbook and a reference for study and practice.Andreas Graw works with data in agriculture. He is also the author of Die digitale Dreifaltigkeit, a history of hardware, software, and artificial intelligence (in German).

From Idea to MVP With Codex: Build a Product in 48 Hours with OpenAI Codex Workflows, and Get Your First Real Users
English | September 17, 2026 | ASIN: B0HBBP25VL | 142 pages | EPUB (True) | 1.68 MB
Every successful software company begins with a single idea. The difference is knowing how to turn that idea into a working product before time and money run out.From Idea to MVP with Codex is a practical guide for entrepreneurs, developers, startup founders, indie hackers, and product builders who want to use AI to design, build, test, and launch a Minimum Viable Product faster than ever. By combining proven startup principles with the power of OpenAI Codex, you'll learn how to validate ideas, generate production-quality code, and accelerate development without sacrificing quality.Whether you are creating your first SaaS platform, mobile application, web application, or AI-powered product, this guide provides a structured roadmap for transforming concepts into working software.Inside you'll discover:* How to turn an idea into an MVP with Codex* Proven startup validation techniques before writing code* AI-powered software development workflows that reduce development time* Strategies for building a Minimum Viable Product with fewer resources* Best practices for product architecture, testing, and deployment* Practical techniques for debugging, refactoring, and improving code with AI* Methods for gathering user feedback and iterating quickly* Common startup mistakes that slow product development and how to avoid themIf you are looking for how to build an MVP, startup product development, OpenAI Codex programming, AI software development, a SaaS startup guide, an AI coding assistant for developers, or a minimum viable product book, this guide provides practical strategies to help you move from concept to launch with greater speed and confidence.Whether your goal is to launch your first startup, build a profitable SaaS business, create an AI-powered application, or become a more productive developer, this book gives you the knowledge and practical techniques needed to build software that solves real problems.Turn your next great idea into a working product with the speed and confidence that AI makes possible.

From Claude to Cash Flow: How to Use Claude AI to Launch and Run a Profitable Online Business in 90 Days Even With No Prior Experience
English | September 18, 2026 | ASIN: B0HFJSTHSF | 144 pages | EPUB (True) | 1.45 MB

Foundations of Mathematics: Arithmetic and Pre-Algebra: A Step-by-Step Guide to Numbers, Problem Solving, and Algebraic Thinking
English | October 9, 2026 | ASIN: B0HMGYF63J | 670 pages | Epub | 1.83 MB
Foundations of Mathematics: Arithmetic and Pre-Algebra offers a step-by-step path from basic number skills to the foundations of algebra. Written for beginners, independent learners, and adults returning to mathematics, it emphasizes understanding how mathematics works and why its methods make sense.Across thirty chapters, readers explore whole numbers, fractions, decimals, ratios, percentages, signed numbers, exponents, and roots before progressing to algebraic expressions, equations, inequalities, and mathematical modeling. Measurement, geometry, coordinate relationships, data, and probability connect these skills to practical situations and prepare readers for Algebra I.Worked examples, guided practice, independent exercises, and answer explanations help readers strengthen their skills. A starting-point assessment helps identify topics to review, while reference appendices provide formulas, conversion tables, a glossary, and reusable study worksheets.By Matthew Meyer, this book encourages readers to approach mathematics with curiosity, check their reasoning, learn from mistakes, and build confidence through steady practice.

Foundations and Engineering of Artificial intelligence: Mathematical Foundations, Deep and Reinforcement Learning, and Large Language Models
English | October 7, 2026 | ASIN: B0HM66PXX1 | 20 Pages | Epub | 35.07 MB
Artificial intelligence is transforming science, engineering, communication, healthcare, robotics, education, industry, and everyday life. Yet understanding modern AI requires more than learning how to call an existing model through an API. It requires understanding the mathematical principles, learning algorithms, neural architectures, optimization methods, and computational processes that make intelligent systems possible.This book provides a progressive introduction to the foundations of contemporary artificial intelligence, connecting mathematics, machine learning, deep learning, reinforcement learning, large language models, and interactive AI systems within a unified framework.The book begins with the mathematical foundations required to understand modern AI, including linear algebra, probability, statistics, information theory, calculus, gradients, Jacobians, Hessians, and optimization. These concepts are then used to develop neural networks from the artificial neuron upward, covering activation functions, multilayer perceptrons, forward propagation, backpropagation, normalization, regularization, residual connections, convolutional neural networks, and recurrent architectures.Reinforcement learning is developed from first principles through agents, environments, states, actions, rewards, policies, value functions, Markov decision processes, Bellman equations, exploration, temporal-difference learning, SARSA, and Q-learning. The discussion then advances to deep reinforcement learning, including Deep Q-Networks, policy-gradient methods, actor-critic algorithms, and Proximal Policy Optimization.A major part of the book is devoted to the foundations and engineering of large language models. Readers are guided through tokenization, embeddings, positional representations, query-key-value projections, causal multi-head self-attention, masking, feed-forward networks, normalization, residual connections, Transformer architectures, next-token prediction, and autoregressive generation.The book also examines modern techniques for improving model capability and efficiency, including rotary position embeddings, RMSNorm, SwiGLU, grouped-query attention, multi-query attention, latent attention mechanisms, and mixture-of-experts architectures.The final chapters explain how pretrained language models are adapted into more capable AI systems through prompting, zero-shot and few-shot learning, supervised fine-tuning, LoRA, QLoRA, preference learning, reward modeling, reinforcement learning from human feedback, Direct Preference Optimization, inference-time reasoning, retrieval, external tool use, safety, behavioral alignment, and multi-step interaction.Throughout the book, mathematical expressions are connected directly to algorithms and computational procedures. The emphasis is not only on what AI systems can do, but on how information is represented, how model parameters are organized, how learning objectives are constructed, how gradients update those parameters, how attention operates, how Transformers generate tokens, and how trained models become components of larger intelligent systems.Designed for upper-level undergraduate and graduate students, engineers, researchers, and technical professionals, this book is suitable for readers in computer science, electrical and computer engineering, data science, robotics, communications, and related disciplines.Its central goal is to provide the mathematical, algorithmic, and engineering foundations needed to understand how modern AI systems are built, trained, adapted, evaluated, and deployed, while preparing readers to contribute to the development of the next generation of artificial intelligence.

Focus and Sharpness: High-Definition Photography
English | September 4, 2026 | ASIN: B0HHTZKZPW | 215 pages | Epub | 8.87 MB
Sharpness in photography is something that obsessively captures every photographer, especially in our early stages. So many factors influence it, and controlling them isn't always easy. Achieving precise, intentional focus in your images is a key mastery toward crafting increasingly spectacular photographs. Identifying the root causes of unsharp images raises awareness and allows you to anticipate errors before they happen.In this new book, we immerse ourselves in the entire universe surrounding the Focus and Sharpness of our images, learning how to shoot in High Resolution.In photography, focusing is not merely a technical step—it is an act of attention. It is deciding what deserves clarity. and what may remain in mystery.Sharpness, for its part, is not just an optical property. It is a form of presence. It is the way an image asserts, defines, and reveals itself with purpose.This book stems from a single essential question:What do we want to show with clarity. and why?Throughout these pages, we will explore focus as a system, a technique, and a language. From automatic modes to manual focus, camera shake to micro-detail, optics to intention.We will analyze how sharpness is built, perceived, and manipulated—and how, at times, softness can express far more than extreme precision. This volume does not aim to impose strict rules, but to offer pathways.Exercises, reflections, comparisons, and choices designed to empower every photographer—whether beginner or advanced—to understand that focusing is looking with intention, and that sharpness is a way of declaring: "These matters."In photography, to focus is about far more than achieving sharpness. It is deciding what deserves attention. It is pointing out, with quiet precision, what truly matters.Focus is a powerful metaphor. Because in life—just as in an image—not everything can be sharp at the same time. You have to choose. You have to prioritize. You have to look with purpose.When a photographer focuses, they are saying: "This is what I want you to see." In doing so, the image transforms into language. Sharpness becomes an assertion; defocus, a suggestion.This book grows from that core belief: focus is not merely a technique, but a way of thinking. A way of observing the world with awareness, crafting images that are not only precise. but meaningful.Welcome to this new journey through attention, clarity, and intent. May each page help you see with greater clarity and decide with deeper purpose. Because learning to focus is, in the end, learning to truly see.

Finite Element Methods in Financial Engineering: From Structural Mechanics to Digital Finance
English | August 31, 2026 | ASIN: B0HH8FTQQ6 | 1195 pages | Epub | 9.57 MB
A complete 32-chapter framework connecting finite element analysis, computational finance and modern digital markets.Finite Element Methods in Financial Engineering: From Structural Mechanics to Digital Finance brings together both volumes of the work in a single Kindle edition, moving from the mathematical foundations of finite element analysis to calibrated computational models for cryptocurrency derivatives and digital financial markets.Volume I develops the mathematical and numerical foundations: variational methods, weak formulations, function and approximation spaces, stability and convergence, Black–Scholes, stochastic volatility, jump dynamics, regularity, free-boundary problems, finite element construction, time discretisation, adaptive approximation and computational verification.Volume II moves from the verified numerical engine to computational finance and market applications. It develops calibration workflows, approximation-space experiments, Heston and Merton benchmarks, the Heston–Merton PIDE, Greeks and sensitivities, cryptocurrency derivative markets, perpetual futures and infinite-horizon pricing.A central theme of the book is Approximation Space Engineering: the deliberate selection and evaluation of finite-dimensional approximation spaces according to the mathematical structure of the pricing problem, the quantity of interest and the computational regime. Rather than assuming that one numerical representation is universally superior, the book examines how accuracy, stability, sensitivities and computational cost change across models and parameter regimes.The complete edition covers:• variational and finite element foundations• function spaces, regularity and convergence• Black–Scholes, Heston and Merton models• stochastic volatility and jump-diffusion pricing• Heston–Merton PIDE formulations• finite element implementation and verification• cryptocurrency option data and calibration• Greeks and sensitivity computation• adaptive and derivative-aware approximation spaces• perpetual cryptocurrency derivatives and funding• approximation-space experiments and regime maps• Approximation Space Adequacy and Approximation Space EngineeringWritten for researchers, quantitative-finance practitioners, computational scientists and advanced students, this complete Kindle edition presents the full progression from structural-mechanics-inspired finite element thinking to a unified numerical framework for contemporary computational and digital finance.Complete Kindle Edition · Volumes I–II · Chapters 1–32