Hala Nelson – författare

Companies are scrambling to integrate AI into their systems and operations. But to build truly successful solutions, you need a firm grasp of the underlying mathematics. This accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory.Engineers, data scientists, and students alike will examine mathematical topics critical for AI--including regression, neural networks, optimization, backpropagation, convolution, Markov chains, and more--through popular applications such as computer vision, natural language processing, and automated systems. And supplementary Jupyter notebooks shed light on examples with Python code and visualizations. Whether you're just beginning your career or have years of experience, this book gives you the foundation necessary to dive deeper in the field.Understand the underlying mathematics powering AI systems, including generative adversarial networks, random graphs, large random matrices, mathematical logic, optimal control, and moreLearn how to adapt mathematical methods to different applications from completely different fieldsGain the mathematical fluency to interpret and explain how AI systems arrive at their decisions

Companies are scrambling to integrate AI into their systems and operations. But to build truly successful solutions, you need a firm grasp of the underlying mathematics. This accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory.

Engineers, data scientists, and students alike will examine mathematical topics critical for AI--including regression, neural networks, optimization, backpropagation, convolution, Markov chains, and more--through popular applications such as computer vision, natural language processing, and automated systems. And supplementary Jupyter notebooks shed light on examples with Python code and visualizations. Whether you''re just beginning your career or have years of experience, this book gives you the foundation necessary to dive deeper in the field.

Understand the underlying mathematics powering AI systems, including generative adversarial networks, random graphs, large random matrices, mathematical logic, optimal control, and moreLearn how to adapt mathematical methods to different applications from completely different fieldsGain the mathematical fluency to interpret and explain how AI systems arrive at their decisions

Companies are scrambling to integrate AI into their systems and operations. But to build truly successful solutions, you need a firm grasp of the underlying mathematics. This accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory.

Engineers, data scientists, and students alike will examine mathematical topics critical for AI--including regression, neural networks, optimization, backpropagation, convolution, Markov chains, and more--through popular applications such as computer vision, natural language processing, and automated systems. And supplementary Jupyter notebooks shed light on examples with Python code and visualizations. Whether you''re just beginning your career or have years of experience, this book gives you the foundation necessary to dive deeper in the field.

Understand the underlying mathematics powering AI systems, including generative adversarial networks, random graphs, large random matrices, mathematical logic, optimal control, and moreLearn how to adapt mathematical methods to different applications from completely different fieldsGain the mathematical fluency to interpret and explain how AI systems arrive at their decisions

Build secure, effective digital twins using proven engineering principles and AI foundations AI Powered Digital Twins: A Guide for Humans, Engineers, and Enterprises provides a unified framework for implementing digital twin systems where AI agents reason, act, and remain governed. Hala Nelson lays out a 12-layer engineering architecture spanning raw data ingestion, semantic spine, event, native core, agent reasoning loops, simulation, Zero Trust security, and governance lifecycle. The architecture targets systems that must perform in critical infrastructure rather than demonstrations. The book delivers a human-centered Standard of Care framework alongside real-world case studies from critical infrastructure and system engineering. Readers gain practical guidance on integrating AI, data engineering, asset tagging, knowledge graphs and quantum computing within digital twin environments. Coverage includes accountability frameworks modeled on the rigor of aviation, healthcare, and civil engineering, plus an AI and data strategy designed to translate directly into measurable business value. The book stresses engineering discipline to bridge the gap between AI investment and real-world delivery. The book contains: How AI agents reason, act, and stay governed within digital twins using Zero Trust security and governance controlsA modular architecture for blending physical and virtual worlds while protecting safety across operational technology environmentsAccountability frameworks modeled on aviation, healthcare, and civil engineering that hold AI systems to a standard of careCase studies demonstrating implementation strategies for manufacturing and integrated system engineeringAn AI and data strategy that translates into measurable business value rather than a policy document on a shelf17 chapters. One architecture. Zero handwaving about security. A must-read for engineers, architects, technical leaders, strategists, and the policymakers who depend on their judgment.

Build secure, effective digital twins using proven engineering principles and AI foundations AI Powered Digital Twins: A Guide for Humans, Engineers, and Enterprises provides a unified framework for implementing digital twin systems where AI agents reason, act, and remain governed. Hala Nelson lays out a 12-layer engineering architecture spanning raw data ingestion, semantic spine, event, native core, agent reasoning loops, simulation, Zero Trust security, and governance lifecycle. The architecture targets systems that must perform in critical infrastructure rather than demonstrations. The book delivers a human-centered Standard of Care framework alongside real-world case studies from critical infrastructure and system engineering. Readers gain practical guidance on integrating AI, data engineering, asset tagging, knowledge graphs and quantum computing within digital twin environments. Coverage includes accountability frameworks modeled on the rigor of aviation, healthcare, and civil engineering, plus an AI and data strategy designed to translate directly into measurable business value. The book stresses engineering discipline to bridge the gap between AI investment and real-world delivery. The book contains: How AI agents reason, act, and stay governed within digital twins using Zero Trust security and governance controls A modular architecture for blending physical and virtual worlds while protecting safety across operational technology environments Accountability frameworks modeled on aviation, healthcare, and civil engineering that hold AI systems to a standard of care Case studies demonstrating implementation strategies for manufacturing and integrated system engineering An AI and data strategy that translates into measurable business value rather than a policy document on a shelf 17 chapters. One architecture. Zero handwaving about security. A must-read for engineers, architects, technical leaders, strategists, and the policymakers who depend on their judgment.