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Category: "Deep Learning"

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  1. NationFiles Stability Index (NFSI): Validation and Verification Report
    NationFiles Stability Index (NFSI): Validation and Verification Report
    Technical Audit of Methodology, Calculation Layers, and Data Integrity.
    Sven Neawolf (Schmidt)

    The official technical audit for the NFSI. Access the complete formulas, weight matrices, and validation logic behind the NationFiles Stability Index to ensure total transparency. Check the live data on https://nationfiles.com

  2. Algorithmic Geopolitics: The 3-Stage Methodology
    Algorithmic Geopolitics: The 3-Stage Methodology
    Normalization, Aggregation, and Weighted Composition within the NationFiles Framework
    Sven Neawolf (Schmidt)

    Discover the logical heart of the NFSI. This paper explains the 3-stage pipeline used to transform heterogeneous OSINT signals into a traceable and auditable geopolitical stability index. Check the live data on https://nationfiles.com

  3. Algorithmic Geopolitics: Methodology of AI-Driven Real-Time Stability Indexing within the NationFiles Framework
    Algorithmic Geopolitics: Methodology of AI-Driven Real-Time Stability Indexing within the NationFiles Framework
    Methodology and Application of AI-Driven Geopolitical Risk Analysis: The Naciro Intelligence Engine
    Sven Neawolf (Schmidt)

    A deep dive into the NationFiles Stability Index (NFSI). Discover how 115+ real-time indicators and the Naciro Intelligence Engine redefine geopolitical risk analysis through transparent, rule-based 15-minute recalibration. Check the live data on https://nationfiles.com

  4. Inside Large Language Models for absolute beginners: Volume II
    Inside Large Language Models for absolute beginners: Volume II
    Simple Arithmetic and beginners Python based approach
    Ritesh Modi

    Most books about ChatGPT explain the magic. This one shows you the math. Inside Large Language Models, Volume I takes a curious beginner from "what is an LLM" to a complete, trained GPT, with nothing more than high-school algebra, a working laptop, and a willingness to read carefully. Every formula is walked through by hand. Every line of code comes with a plain-English explanation. By the end you will have built, trained, and run your own transformer from scratch, and you will know exactly what is happening inside. No PhD or Data Science required. No prior machine learning needed. Just curiosity and a calculator.

  5. Inside Large Language Models for absolute beginners: Volume I
    Inside Large Language Models for absolute beginners: Volume I
    Simple Arithmetic and beginners Python based approach
    Ritesh Modi

    Most books about ChatGPT explain the magic. This one shows you the math. Inside Large Language Models, Volume I takes a curious beginner from "what is an LLM" to a complete, trained GPT, with nothing more than high-school algebra, a working laptop, and a willingness to read carefully. Every formula is walked through by hand. Every line of code comes with a plain-English explanation. By the end you will have built, trained, and run your own transformer from scratch, and you will know exactly what is happening inside. No PhD or Data Science required. No prior machine learning needed. Just curiosity and a calculator.

  6. Force-Information-Time
    Force-Information-Time
    A structural framework for understanding how complex systems evolve through force, information, time, and constraint
    Qien Huang

    From quantum and molecules to cells, individuals, organizations, nations, and civilizations—why do clearly defined hierarchical structures emerge? Why does evolution often manifest as a repeating rhythm of "oscillation—stability—aggregation—re-stability"? Why do many systems fail not because of insufficient power or lack of information, but because the "pace of doing things" is wrong?

  7. Modern Introduction to Data Science
    Modern Introduction to Data Science
    Mastering Analytics, Machine Learning, and Data-Driven Insights
    Alex R. Insight

    Master the future of technology with this definitive guide to Modern Data Science. Unlock actionable insights through Analytics, Machine Learning, and Big Data strategies. Perfect for beginners and pros wanting a logic-first approach to data-driven decision making.

  8. Beyond Context Graphs: Agentic Memory, Cognitive Processes, and Promise Graphs
    Beyond Context Graphs: Agentic Memory, Cognitive Processes, and Promise Graphs
    Enterprise level agent in user pocket
    Volodymyr Pavlyshyn

    AI engines are booming, and the more we work with agentic systems, the more we see that we need something to make them work at the enterprise level. We're quite active in exploring ideas around context graphs, decision traces, and supporting explainability—giving agents the ability to make more aware and company-aligned decisions.But this makes sense not only for enterprises, but for users and individuals building personal agents as well. Unfortunately, we have zero-to-none inclination on how to actually build a context graph.I'll try to explain how to build something like a context graph—but go beyond it. I deeply believe that to make this work, we need specific agentic memory and a set of cognitive processes that truly help agents use this memory and learn from experience and data.That's why this is the Book: Beyond Context Graphs—with a focus on real-life enterprise tasks and how to make agents make better decisions and, let's say, hallucinate less.

  9. Mastering Advanced Time Series Forecasting in Python: Probabilistic, Hierarchical, and Foundation Models
    Mastering Advanced Time Series Forecasting in Python: Probabilistic, Hierarchical, and Foundation Models
    Master advanced forecasting with Python using machine learning, deep learning, and cutting-edge foundational models. Learn hierarchical and probabilistic forecasting, forecastability, metrics, and scalable pipelines. Build robust, real-world forecasting systems with production-ready code and expert guidance.
    Valery Manokhin

    Mastering Advanced Time Series Forecasting in Python is the definitive sequel to the #1 forecasting bestseller. Designed for practitioners who want to go beyond ARIMA and basic ML, this book takes you deep into probabilistic forecasting, hierarchical coherence, and cutting-edge foundation models—backed by production-ready Python code. Learn how to assess forecastability, build scalable pipelines, quantify uncertainty, and deploy systems that deliver real business impact. Written by a globally recognized expert whose methods power multimillion-dollar decisions, this is the practical, honest, and advanced guide every data scientist, ML engineer, and quantitative professional needs to master modern forecasting.

  10. Generative AI for Science
    Generative AI for Science
    A Hands-On Guide for Students and Researchers
    J. Paul Liu

    Bridge AI and science with this hands-on guide. Whether you're a researcher learning ML or an engineer entering scientific applications, build real systems across chemistry, biology, physics & climate. Master Transformers, Diffusion Models & GNNs for scientific discovery. 500+ pages, 50+ Colab notebooks. Design molecules, predict proteins, accelerate climate models—all hands-on, zero setup required.

  11. Mastering Forecasting Metrics & Accuracy: For Data Science and Beyond

    Mastering Forecasting Metrics & Accuracy: For Data Science and BeyondForecasting models are only as good as the metrics used to measure them. Yet many teams still rely on outdated or misleading measures like MAPE. This book is the first comprehensive, practitioner-friendly guide dedicated entirely to forecast evaluation metrics — blending clear theory, Python recipes, and real-world case studies.Learn how to avoid common pitfalls, measure bias, handle intermittent demand, and apply advanced metrics like MASE, RMSSE, CRPS, pinball loss, and calibration scores. Each chapter includes formulas, code, and visuals to make concepts easy to apply.Perfect for data scientists, ML engineers, analysts, researchers, and industry professionals in retail, finance, and energy. No heavy math required.Living book: buy once, get free lifetime updates.Measure what matters.

  12. Document Like a Developer: Technical Writing with AI Tools
    Document Like a Developer: Technical Writing with AI Tools
    Write Once, Scale Everywhere: The New Era of Technical Communication
    Kevin Languedoc

    Documentation isn’t just a chore—it’s a craft. And with AI tools at your side, it’s finally scalable, smart, and developer-friendly. Document Like a Developer: Technical Writing with AI Tools is your blueprint for building docs that ship with your code, evolve with your product, and speak your users’ language. Learn how to enforce style guides, automate glossary checks, run documentation sprints, and measure impact with precision. Whether you're a solo dev, a startup team, or scaling across an enterprise, this book gives you the workflows, templates, and mindset to treat documentation like a first-class citizen. Stop writing throwaway manuals. Start building living systems of clarity. The future of technical writing is here—and it’s automated, collaborative, and built for velocity.

  13. Super Study Guide: 트랜스포머와 대형 언어 모델
    Super Study Guide: 트랜스포머와 대형 언어 모델
    Afshine Amidi, Shervine Amidi, and Yongjin Kim

    이 책은 면접 준비, 프로젝트 진행, 또는 순수한 지적 호기심을 위해 대규모 언어 모델의 내부 구조와 작동 원리를 이해하고 싶은 모든 분들을 위한, 그림으로 설명하는 핵심 가이드입니다.

  14. Super Study Guide: ตัวแปลงและแบบจำลองภาษาขนาดใหญ่

    หนังสือเล่มนี้เป็นคู่มือฉบับกระชับพร้อมภาพประกอบ สำหรับผู้ที่อยากเข้าใจการทำงานภายในของแบบจำลองภาษาขนาดใหญ่ ในบริบทการสัมภาษณ์ ทำโครงการ หรือเพื่อสนองตอบความใคร่รู้ของตนเอง

  15. Super Study Guide: Transformers y Grandes Modelos de Lenguaje
    Super Study Guide: Transformers y Grandes Modelos de Lenguaje
    Afshine Amidi, Shervine Amidi, laramaktub, and Steven Van Vaerenbergh

    Este libro es una guía concisa e ilustrada para cualquiera que desee comprender el funcionamiento interno de los Grandes Modelos de Lenguaje, ya sea de cara a realizar entrevistas, proyectos o para satisfacer su curiosidad.