Books

 Closing Thoughts“Mathematics for Artificial Intelligence – II (Statistics and Optimization)” is more than a textbook—it is a guidebook for mastering the mathematics behind AI. While Volume I laid the foundation and your other book covered data science statistics, this volume pushes students, researchers, and practitioners into the advanced territory where modern AI thrives.Whether you want to become a machine learning engineer, AI researcher, data scientist, or academic scholar, mastering the material in this book will give you the edge to not only use AI tools but also innovate and push the boundaries of artificial intelligence.

Benefits of Studying This Book 1. Deep Conceptual Understanding You will understand why AI algorithms work, not just how to run them. This allows you to innovate, debug, and improve models. 2. Career Advantage Strong mathematical foundations make you stand out in interviews for AI, ML, and DS roles. Many recruiters test candidates on linear algebra and probability skills. 3. Research Readiness Postgraduate students and researchers can directly apply these mathematical tools to design and analyze experiments. 4. Practical AI Skills Python-based implementation examples ensure that you can directly apply mathematical concepts in real-world AI systems. 5. Interdisciplinary Edge Mathematics learned here is not limited to AI — it can be applied in robotics, quantum computing, finance, bioinformatics, and more. How This Book Helps After StudyAfter completing this book, you will be able to:·        Build AI models from scratch, knowing exactly what mathematical operations are happening inside.·        Optimize models for performance using a deep understanding of linear algebra operations.·        Analyze and interpret model predictions probabilistically.·        Handle uncertainty and noise in datasets effectively.·        Implement advanced AI concepts like PCA, SVD, Bayesian inference, and Markov models without relying solely on pre-built libraries.This knowledge will directly help in:·        Academics: Scoring well in AI/ML/DS university courses.·        Industry: Working as an AI engineer, data scientist, ML engineer, or research scientist.·        Competitive Exams: Preparing for GATE, NET, and other AI-related exams where mathematics is heavily tested.·        Research: Publishing papers where mathematical rigor is required to explain new AI techniques. 

WHO SHOULD READ THIS BOOK?This book is ideal for:·        BCA, MCA, B.Tech, M.Tech students·        UGC NET aspirants·        AI/ML researchers·        Data scientists·        AI developers·        University professors·        PhD scholars·        Industry professionals working with black-box models·        Anyone who wants mathematical clarity on XAIIts writing style balances mathematical rigor with readability, making it useful for self-study and classroom use. TEACHING & LEARNING BENEFITS·        50+ diagrams, proofs, and mathematical derivations.·        Step-by-step logical flow for each model.·        Case studies from healthcare, finance, law, and engineering.·        Practical coding references (without over-reliance on tools).·        Integration of statistics, calculus, causality, and deep learning.·        Real-world examples for intuitive understanding.·        Problems at the end of each chapter (optional addition).Instructors can adopt this book for academic courses in:·        Explainable AI·        Machine Learning·        Statistical Inference·        Causality·        Artificial Intelligence Foundations·        Deep Learning Interpretability UNIQUE CONTRIBUTIONS OF THIS BOOKUnlike other XAI or ML books, this work by Anshuman Mishra offers:·        Mathematical derivations for SHAP, IG, LIME, and other explainability tools.·        Original proofs for fairness properties in attribution methods.·        Detailed causal diagrams and do-calculus explanations.·        A structured approach to XAI evaluation metrics.·        Coverage of transformer explainability—rare in academic books.·        Clarity in blending classical mathematical theory with modern AI systems.This makes the book a reference-level resource for the next decade of AI learning.

WHO SHOULD READ THIS BOOK?This book is ideal for:·        BCA, MCA, B.Tech, M.Tech students·        UGC NET aspirants·        AI/ML researchers·        Data scientists·        AI developers·        University professors·        PhD scholars·        Industry professionals working with black-box models·        Anyone who wants mathematical clarity on XAIIts writing style balances mathematical rigor with readability, making it useful for self-study and classroom use. TEACHING & LEARNING BENEFITS·        50+ diagrams, proofs, and mathematical derivations.·        Step-by-step logical flow for each model.·        Case studies from healthcare, finance, law, and engineering.·        Practical coding references (without over-reliance on tools).·        Integration of statistics, calculus, causality, and deep learning.·        Real-world examples for intuitive understanding.·        Problems at the end of each chapter (optional addition).Instructors can adopt this book for academic courses in:·        Explainable AI·        Machine Learning·        Statistical Inference·        Causality·        Artificial Intelligence Foundations·        Deep Learning Interpretability UNIQUE CONTRIBUTIONS OF THIS BOOKUnlike other XAI or ML books, this work by Anshuman Mishra offers:·        Mathematical derivations for SHAP, IG, LIME, and other explainability tools.·        Original proofs for fairness properties in attribution methods.·        Detailed causal diagrams and do-calculus explanations.·        A structured approach to XAI evaluation metrics.·        Coverage of transformer explainability—rare in academic books.·        Clarity in blending classical mathematical theory with modern AI systems.This makes the book a reference-level resource for the next decade of AI learning.

What if Kant’s theory of knowledge were not merely a philosophical speculation, but an implicit description of the very architecture of the human brain?In this ambitious philosophical work, Marcel Chelba proposes a new epistemological paradigm: Stereoperspectival Epistemology — a theory of knowledge integrating Kantian transcendental philosophy, modern neuroscience, brain hemispheric asymmetry, cybernetics, and the epistemological crises of contemporary science.

Geopolitik ist zu langsam. Entdecken Sie, wie die Naciro Engine KI nutzt, um den Puls der Nationen in Echtzeit zu messen – mit einer 15-minütigen Rekalibrierung von globaler Wahrheit und Macht. Check the live data on https://nationfiles.com

Geopolitics is too late. Discover how the Naciro Engine uses AI to measure the pulse of nations in real-time, providing a 15-minute recalibration of global truth and power. Check the live data on https://nationfiles.com

See how NationFiles compares to the world's leading stability indices. This report benchmarks the NFSI against traditional models to demonstrate the power of real-time geopolitical data. Check the live data on https://nationfiles.com

A concentrated scientific overview of the NationFiles framework. This monograph details the methodology and application of AI-driven real-time stability indexing for research and peer review. Check the live data on https://nationfiles.com

Discover the LPU architecture. This note details the specialized inference stack used for sub-50 ms latency and deterministic data embedding within the Naciro Intelligence Engine. Check the live data on https://nationfiles.com

Experience the speed of modern geopolitics. This overview details the NFSI’s 15-minute recalibration cycle and the global data infrastructure powering the NationFiles framework. Check the live data on https://nationfiles.com

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

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

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

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.