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AI Mathematics Ladder - Book 2 AI Mathematics - Building Structure Vectors, Matrices, and Transformations for Intelligent Machines: From intuition to ... ... Intelligence from First Principles)
English | March 9, 2026 | ASIN: B0GRW2HHZD | 280 pages | Epub | 6.57 MB
Artificial Intelligence is not magic.It is mathematics carefully layered.AI Mathematics Ladder - Book 2: Building Structure continues the journey from intuition to mathematical clarity.In Book 1, readers discovered the foundations of AI thinking.Now the ladder climbs higher.This book introduces the structured mathematical language that powers intelligent machines:* Vectors as structured data* Matrices as transformation engines* Dot products as similarity* Probability as uncertainty* Logistic regression as the first AI classifier* Optimization as the learning processRather than overwhelming readers with complex formulas, the book builds ideas step by step from first principles, using clear explanations, intuitive reasoning, and simple examples.By the end of this book, readers will understand how:Data becomes vectorsVectors become transformationsTransformations become predictionsPredictions become learning systemsThis is the mathematical bridge between intuition and real machine learning.Perfect for:* AI beginners* data science students* programmers entering machine learning* curious learners who want to understand AI without fear of mathematicsThe ladder continues.

AI Mathematics - Deep Intelligence Systems Neural Networks, Attention, and Scaling: Understanding the Mathematical Architecture of Modern Artificial ... Intelligence from First Principles Book 4)
English | March 10, 2026 | ASIN: B0GHSV1RV5 | 246 pages | Epub | 7.11 MB
AI Mathematics Ladder - Level 4AI Mathematics - Deep Intelligence SystemsNeural Networks, Attention, and ScalingUnderstanding the Mathematical Architecture of Modern Artificial IntelligenceAuthor: Ravindra NayakArtificial Intelligence is often described as magic.But behind every powerful AI system lies a carefully layered mathematical architecture.AI Mathematics - Deep Intelligence Systems is the fourth book in the AI Mathematics Ladder, a progressive series designed to help readers move step-by-step from intuitive mathematical thinking to a deep understanding of modern AI.In this volume, the journey moves beyond basic neural networks into the core systems powering modern artificial intelligence.You will explore how deep learning systems actually work-from representation geometry and information flow to the transformer architectures that power today's most advanced models.Instead of overwhelming formulas, this book explains complex concepts through first-principles reasoning, intuitive narratives, and structured mathematical insight.By the end of the book, readers will understand how modern AI systems are built-from gradients and optimization to attention mechanisms and large-scale training.Inside This Book You Will Discover* Why deep neural networks work* How representation learning transforms raw data into meaning* The mathematics behind softmax, loss landscapes, and optimization* Why attention mechanisms revolutionized machine learning* How transformers became universal AI architectures* The geometry of knowledge inside neural networks* Why large models scale successfully* The full system view of artificial intelligenceWho This Book Is ForThis book is ideal for:* AI researchers and engineers* data scientists and machine learning practitioners* students studying artificial intelligence* readers who want a deep conceptual understanding of modern AINo unnecessary complexity.No missing mathematical intuition.Just a clear path toward understanding the systems that power modern artificial intelligence.Climb the ladder.Understand the architecture.See how intelligence emerges from mathematics.

AI Mastery for Beginners Step-by-Step: From Complete Novice to Confident User in 30 Days
English | January 1, 2026 | ASIN: B0GDMY3BFX | 307 pages | Epub | 811.69 KB
You don't need a tech background to use AI. You just need the right guide.Artificial intelligence tools are transforming how people work, create, and solve problems-but most resources assume you already know the basics. If you've felt left behind by the AI conversation, this book meets you exactly where you are and takes you somewhere remarkable.What's InsideThis isn't a theoretical overview or a collection of buzzwords. It's a practical, hands-on program designed for complete beginners who want real results. You'll learn how AI tools actually work, how to communicate with them effectively, and how to integrate them into your daily routine-without confusion, overwhelm, or wasted time.The core of this book is a structured 30-day roadmap that builds your skills progressively. Week by week, you'll move from your first interaction to full integration, developing habits and workflows that stick.What You'll GainSet up your complete AI toolkit and start using it immediatelyWrite prompts that consistently deliver useful, high-quality resultsSave hours each week on writing, research, and routine tasksUse AI for creative projects, brainstorming, and problem-solvingAvoid the common mistakes that frustrate most beginnersBuild sustainable habits that make AI a natural part of your workflowUnderstand what AI can and cannot do-so you use it wiselyWho This Book Is ForThis guide is for anyone who wants to use AI tools confidently but doesn't know where to start. No coding required. No technical experience necessary. Just curiosity and a willingness to learn.Whether you want to boost your productivity, explore creative possibilities, or simply understand what everyone's talking about, this book gives you a clear path forward. Open it today-and in 30 days, you'll wonder how you ever worked without these tools.

AI ENGINEERING HANDBOOK: A Practical Guide to Machine Learning, Deep Learning, Generative AI, Python, and Building Real World AI Applications .
English | May 24, 2026 | ASIN: B0GZCKNP8S | 153 pages | Epub | 605.89 KB
Artificial Intelligence is transforming the world faster than ever before, and the demand for skilled AI engineers continues to grow across every industry. Whether you are a beginner exploring artificial intelligence for the first time or a developer looking to expand your technical skills, AI Engineering Handbook provides a practical and easy to understand roadmap for mastering modern AI development.This comprehensive guide introduces the core foundations of AI engineering while helping readers build real world skills in machine learning, deep learning, generative AI, neural networks, prompt engineering, Python programming, and AI automation.Inside this book, you will learn how artificial intelligence systems work, how machine learning models are trained, and how AI engineers design intelligent applications used in today's technology driven world. From understanding data and algorithms to building AI powered applications, this handbook breaks down complex concepts into clear, practical lessons that are accessible for beginners and valuable for aspiring professionals.You will also explore the latest advancements in generative AI, large language models, AI assistants, and intelligent automation tools that are reshaping businesses and careers globally. With practical explanations and real world examples, this book equips you with the knowledge needed to confidently begin your journey in AI engineering.Inside This Book, You Will Discover:✔ Artificial Intelligence fundamentals explained in simple terms✔ Machine Learning and Deep Learning essentials✔ Python programming for AI development✔ Neural Networks and Generative AI concepts✔ Prompt Engineering and AI automation workflows✔ Real world AI applications and deployment basics✔ AI ethics, security, and responsible AI practices✔ Career guidance for future AI engineers✔ Beginner friendly explanations with practical insightsWhether your goal is to become an AI engineer, machine learning developer, data scientist, software engineer, or technology entrepreneur, this book gives you the foundational knowledge and practical understanding needed to succeed in one of the world's fastest growing industries.If you are ready to explore the future of artificial intelligence, build valuable technical skills, and understand the technologies shaping tomorrow's digital world, then AI Engineering Handbook is the perfect place to begin.Start your AI journey today and discover how to build intelligent systems that can transform ideas into innovation.

AI Cost Optimization System Design for AI Engineers: Design scalable LLM, RAG, and agentic AI systems that reduce token spend, control inference costs, and improve production ROI.
English | June 11, 2026 | ASIN: B0H4XDHVZR | 123 pages | PDF | 3.25 MB
Stop Letting Your AI Architecture Quietly Destroy Your Profit MarginsBuilding an AI system that works is only the beginning. The real challenge is designing one that remains fast, reliable, and financially sustainable as usage grows.AI Cost Optimization System Design for AI Engineers is a practical guide to controlling the cost of production LLM, RAG, and agentic AI systems without sacrificing the quality users expect.Rather than focusing on temporary provider prices, this book teaches durable engineering principles for understanding where AI spend originates, measuring cost per successful task, and selecting the highest-impact optimization strategy.Inside, you will learn how to:Design cost-aware architectures for LLM applicationsReduce input, output, reasoning, and hidden-token usageSelect and route requests to the right modelBuild efficient RAG pipelines with optimized retrieval and rerankingIncrease savings through prompt, semantic, and provider cachingPrevent runaway agent loops, tool calls, and multi-agent workflowsOptimize GPU inference, batching, and serving infrastructureMeasure cost per request, user, feature, and successful taskEstablish AI FinOps dashboards, budgets, alerts, and governanceBalance cost, quality, latency, reliability, and business marginEach chapter combines system-design principles with formulas, architecture diagrams, implementation patterns, production case studies, optimization checklists, common mistakes, summary notes, and senior-level interview questions.The book also includes practical projects covering model routing, RAG optimization, agent budget control, caching, observability, evaluation, and cost-aware AI platform design.This book is ideal for:AI and machine learning engineersLLMOps, MLOps, and platform engineersAgentic AI developersSolutions architects and technical leadersFounders building AI-powered productsEngineers preparing for senior AI system-design interviewsWhether you are reducing an unexpectedly large AI bill, designing a new production platform, or preparing for your next engineering interview, this book gives you the frameworks needed to make every AI request economically defensible.Build AI systems that do more than work. Build systems that can afford to scale.

AI Cinematic Filmmaking: Pre-Production: The Complete Workflow for AI Filmmakers: From Story to Animatic (THE AI CINEMATIC SERIES Book 5)
English | May 11, 2026 | ASIN: B0H1DYD485 | 252 pages | Epub | 2.40 MB
AI Cinematic Filmmaking: Pre-Production is a practical workflow guide for filmmakers, creators, writers, and AI artists who want to turn ideas into structured cinematic projects. Instead of focusing on hype or endless prompt tricks, the book breaks down the real planning process behind AI filmmaking.Inside, you'll learn how to:analyze stories for AI adaptationbuild a Story Biblebreak scripts into cinematic scenescreate shot lists and visual language systemsdesign consistent characters and locationsorganize production assetsdevelop storyboards and animaticsprepare projects for smoother AI video generationThe workflow is designed for real production use, whether you are creating:short filmscinematic videostrailersweb seriesexperimental projectsindie feature filmsThis book teaches that methodology, end to end, using Ambrose Bierce's "An Occurrence at Owl Creek Bridge" as a worked example throughout. Every prompt is shown. Every output is explained. Every creative decision is made transparent.Plus two production tools: the Prompt Library with a 9-Day Schedule, and the Suffix Library Generator that compresses your bibles into paste-ready fragments for image and video generation using chatgpt, Claude, Gemini , GPTimage, NanoBanana, Veo, Kling, Higgsfiled cinema studio, Seedance, etc. Solo filmmakers, writer-directors, AI artists ready to make full films, traditional filmmakers exploring AI workflows, and anyone with a story worth telling. Book 1 of 3. Book 2 covers Production (video generation, voice, rough cut). Book 3 covers Post-Production (final audio, score, color, delivery).

AI Builds Itself: Recursive Self-Improvement in 2026 (Toward Artificial SuperIntelligence Book 1)
English | March 31, 2026 | ASIN: B0FRBBGPG6 | 199 pages | Epub | 651.66 KB
The machines are already improving themselves. You just have not been told how fast.In the spring of 2026, Claude began writing between seventy and ninety percent of the code used to train its own next version. AlphaEvolve spent over a year optimizing the training process for the very models that powered it, recovering enough wasted compute across Google's global fleet to power a small country's worth of servers. OpenAI's Codex debugged the training pipeline that produced it. And a 630-line Python script showed that anyone with a single GPU could run autonomous AI research experiments overnight."AI Builds Itself" is the first comprehensive survey of recursive self-improvement as it exists right now. Written by H. Peter Alesso, an author with deep expertise in AI systems, web intelligence, and emerging technology, this book maps the full landscape: the organizations driving the science, the technical architectures that make self-improvement possible, the billion-dollar startups founded by the architects of AlphaGo and GPT-4, and the safety researchers warning that the feedback loops powering progress are the same loops that could outrun human oversight.This book is for technology professionals, AI researchers, policymakers, investors, and informed general readers who want to understand the most consequential technological development of the decade, not through speculation or hype, but through documented evidence, cited research, and careful analysis of what the leading minds in the field are actually building.You will learn how AlphaEvolve discovers algorithms that outperform fifty-six-year-old mathematical records. How the Darwin Godel Machine rewrites its own source code and evaluates whether the rewritten version is better at rewriting itself. How Karpathy's AutoResearch framework turns plain-language instructions into an overnight autonomous research laboratory. How Constitutional AI attempts to keep self-improving systems aligned with human values. And why the AI 2027 forecast, graded at sixty-five percent of its predicted pace, still projects transformative change within the next eighteen months.One appendix with a working implementation you can run yourself. A comprehensive glossary and bibliography. No hype. No science fiction. Just the clearest account available of what is happening inside the world's most advanced AI laboratories and what it means for everyone outside them.

AI Automation with n8n: Build Smart Workflows Using AI Tools: A Practical Guide to Automating Tasks, Integrating AI Tools, and Creating Intelligent Workflows Without Complex Coding
English | March 7, 2026 | ASIN: B0GRPDF17B | 199 pages | Epub | 7.00 MB
Automation is becoming one of the most valuable digital skills. Tools like n8n allow anyone to connect apps, automate tasks, and build powerful workflows without writing complex code.AI Automation with n8n explains how to combine workflow automation with modern AI tools to create systems that save time and handle repetitive tasks automatically.This book takes a practical approach, helping readers understand not only how automation works, but how to apply it in real projects.In this book you will learn:* How workflow automation platforms work* How to install and configure n8n step-by-step* How nodes, triggers, and workflow logic operate* How to integrate APIs and external services* How to use AI models inside automation workflows* How to automate content creation and communication* How to process and analyze data automatically* How to build AI-powered assistants and automation toolsReal examples throughout the book show how to automate tasks such as:* generating content using AI* summarizing emails and messages* categorizing support requests* scheduling social media posts* extracting data from documentsBy the end of this guide, you will have the knowledge required to design your own intelligent workflows using n8n and modern AI tools.

AI: Anatomy of Modern Power : Data, Algorithms, and the New Global Struggle for Control (Anatomy of Modern War Book 5)
English | May 9, 2026 | ASIN: B0H15RZRGG | 223 pages | Epub | 9.64 MB
Artificial intelligence is no longer a future technology. It is already one of the most powerful forces reshaping the global balance of power.AI: Anatomy of Modern Power explores the true nature of AI beyond hype, myths, and simplifications. It examines how artificial intelligence is gradually transforming into a strategic infrastructure that affects the economy, politics, defense, information, and the very functioning of modern societies.The book begins with the foundations of artificial intelligence: data, algorithms, computational power, and the invisible infrastructures that make AI possible. It then analyzes how AI evolves from a technological tool into a mechanism of influence and geopolitical power.Through an examination of the global race for computational supremacy, competition over semiconductors, data as a strategic resource, and the concentration of digital power, the book reveals that the battle for AI is not merely about innovation. It is about control over the foundations of the new global order.At the same time, the book explores the social and political consequences of the algorithmic age: surveillance,information manipulation, algorithmic influence, labor automation, digital dependency, and the transformation of the concept of authority itself.The book does not treat artificial intelligence merely as a technological issue. It treats it as a new system of power.In a world where data becomes raw material, algorithms become mechanisms of influence, and computational power becomes geopolitical advantage, understanding AI is no longer optional. It is a necessity.AI: Anatomy of Modern Power is a strategic analysis of artificial intelligence as the defining arena of competition in the 21st century.

AI Agents in Production: A Practical Guide to Building Reliable LLM-Powered Systems with Claude, GPT, and MCP
English | May 17, 2026 | ASIN: B0H23MM17L | 79 pages | PDF | 37.06 MB
Stop Building Demos. Start Shipping Production-Ready AI Agents.AI agents are transforming software engineering, but deploying a reliable system that runs at scale is entirely different from running a local prototype. AI Agents in Production is your definitive, hands-on guide to taking large language model (LLM) applications from fragile experiments to robust, scalable enterprise systems. Written specifically for working software engineers, this book provides the practical blueprints you need to build reliable agentic systems using Claude, GPT, and the Model Context Protocol (MCP).What You Will LearnDesign Robust Architectures: Master ReAct, Plan-and-Execute, and advanced multi-agent system patterns.Integrate Real-World Tools: Use the Model Context Protocol (MCP) to seamlessly connect agents to external APIs with Python and Node.Implement Bulletproof Reliability: Master advanced error handling, retry logic, and graceful degradation for LLM variability.Instrument for Observability: Track traces, metrics, and agent behavior logs to monitor your systems in real-time.Defend Against Vulnerabilities: Secure your agents against prompt injection, unauthorized tool access, and model drift.Scale for the Enterprise: Optimize throughput and cost using message queues, parallelism, and efficient context memory management.Automate Evaluation: Build continuous regression testing and quality evaluation pipelines for non-deterministic AI.If you are tired of theoretical AI research papers and want actionable, production-focused engineering strategies, this book is for you. Every chapter includes practical implementation checklists you can apply to your codebase today. Scroll up and grab your copy to build AI agents that actually work in production!