Dr Upendra Singh (24 results)

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Paperback. Condition: new. Paperback. What if the earliest signs of Parkinson's and Alzheimer's disease are already hidden in the data and machine learning can reveal them before symptoms appear?Signals Before Symptoms is a practical, rigorous guide to building trustworthy machine-learning systems for early-detection research. Written for researchers, engineers, clinicians, and postgraduate students, it explores meaningful signals across MRI, PET, DaTSCAN, speech, gait, wearables, handwriting, digital biomarkers, and clinical, cognitive, and genetic data.Move beyond inflated accuracy. Learn to prevent data leakage, harmonise multisite datasets, manage missing modalities, evaluate performance at real-world prevalence, test explanations, and communicate uncertainty honestly. Twelve revealing case files expose why promising models fail, while a complete worked study demonstrates the journey from research question to defensible conclusion.Featuring exercises, reporting checklists, dataset guidance, code recipes, and global perspectives on regulation, ethics, deployment, and equity, this book helps you create research that is reproducible, clinically meaningful, and built to withstand scrutiny. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardcover. Condition: new. Hardcover. What if the earliest signs of Parkinson's and Alzheimer's disease are already hidden in the data and machine learning can reveal them before symptoms appear?Signals Before Symptoms is a practical, rigorous guide to building trustworthy machine-learning systems for early-detection research. Written for researchers, engineers, clinicians, and postgraduate students, it explores meaningful signals across MRI, PET, DaTSCAN, speech, gait, wearables, handwriting, digital biomarkers, and clinical, cognitive, and genetic data.Move beyond inflated accuracy. Learn to prevent data leakage, harmonise multisite datasets, manage missing modalities, evaluate performance at real-world prevalence, test explanations, and communicate uncertainty honestly. Twelve revealing case files expose why promising models fail, while a complete worked study demonstrates the journey from research question to defensible conclusion.Featuring exercises, reporting checklists, dataset guidance, code recipes, and global perspectives on regulation, ethics, deployment, and equity, this book helps you create research that is reproducible, clinically meaningful, and built to withstand scrutiny. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardcover. Condition: new. Hardcover. Most coding interview books teach answers. This book teaches you how to think, communicate, test, and improve under pressure.Data Structures and Algorithms in C++ takes readers from basic loops and functions to interview-ready problem solving through 12 core patterns, 26 focused chapters, nine realistic interview transcripts, and more than 200 practice problems. Every structure is built from first principles in C++17, every major operation includes clear time and space complexity, and every worked example shows not only what to code, but also what to say.Readers learn to recognize recurring problem patterns, choose the right data structure, explain trade-offs, recover from wrong starts, debug with the E-D-G-E method, and connect interview techniques to real production systems. Topics include arrays, linked lists, stacks, queues, hashing, trees, heaps, tries, graphs, greedy algorithms, dynamic programming, bits, and modern C++.With scored transcripts, capstone projects, a readiness test, practical checklists, and a four-week study plan, this book is a complete guide for students, graduates, and professionals preparing to solve confidently, communicate clearly, and perform strongly in technical interviews at leading technology companies worldwide. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardcover. Condition: new. Hardcover. AI Security Engineering is a practical, rigorous guide to building cybersecurity systems that use machine learning, large language models, AI agents, retrieval-augmented generation, MCP, and Python-without sacrificing security, reproducibility, or operational realism. Designed for students, researchers, engineers, and security professionals, the book moves from foundational concepts and intrusion detection to phishing, malware, IoT security, adversarial machine learning, explainability, calibration, federated learning, and production deployment. It also addresses the new risks created by LLM applications and autonomous agents, including prompt injection, tool misuse, memory poisoning, vector-store exposure, supply-chain threats, and AI incident response. Every major topic is connected to runnable code, realistic datasets, evaluation discipline, and deployment decisions. Readers learn not only how to build models, but how to test whether results are trustworthy, choose thresholds based on analyst capacity, detect leakage, manage drift, and create evidence that survives review. With case studies, deployment checklists, algorithm cards, dataset cards, exercises, and reproducible workflows, AI Security Engineering turns AI-powered security from an experiment into a defensible engineering practice for modern security teams operating in rapidly changing, adversarial, and increasingly agent-driven digital environments. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condition: new. Paperback. What if the earliest signs of Parkinson's and Alzheimer's disease are already hidden in the data and machine learning can reveal them before symptoms appear?Signals Before Symptoms is a practical, rigorous guide to building trustworthy machine-learning systems for early-detection research. Written for researchers, engineers, clinicians, and postgraduate students, it explores meaningful signals across MRI, PET, DaTSCAN, speech, gait, wearables, handwriting, digital biomarkers, and clinical, cognitive, and genetic data.Move beyond inflated accuracy. Learn to prevent data leakage, harmonise multisite datasets, manage missing modalities, evaluate performance at real-world prevalence, test explanations, and communicate uncertainty honestly. Twelve revealing case files expose why promising models fail, while a complete worked study demonstrates the journey from research question to defensible conclusion.Featuring exercises, reporting checklists, dataset guidance, code recipes, and global perspectives on regulation, ethics, deployment, and equity, this book helps you create research that is reproducible, clinically meaningful, and built to withstand scrutiny. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condition: new. Paperback. What if the earliest signs of Parkinson's and Alzheimer's disease are already hidden in the data and machine learning can reveal them before symptoms appear?Signals Before Symptoms is a practical, rigorous guide to building trustworthy machine-learning systems for early-detection research. Written for researchers, engineers, clinicians, and postgraduate students, it explores meaningful signals across MRI, PET, DaTSCAN, speech, gait, wearables, handwriting, digital biomarkers, and clinical, cognitive, and genetic data.Move beyond inflated accuracy. Learn to prevent data leakage, harmonise multisite datasets, manage missing modalities, evaluate performance at real-world prevalence, test explanations, and communicate uncertainty honestly. Twelve revealing case files expose why promising models fail, while a complete worked study demonstrates the journey from research question to defensible conclusion.Featuring exercises, reporting checklists, dataset guidance, code recipes, and global perspectives on regulation, ethics, deployment, and equity, this book helps you create research that is reproducible, clinically meaningful, and built to withstand scrutiny. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: new. Paperback. AI Security Engineering is a practical, rigorous guide to building cybersecurity systems that use machine learning, large language models, AI agents, retrieval-augmented generation, MCP, and Python-without sacrificing security, reproducibility, or operational realism. Designed for students, researchers, engineers, and security professionals, the book moves from foundational concepts and intrusion detection to phishing, malware, IoT security, adversarial machine learning, explainability, calibration, federated learning, and production deployment. It also addresses the new risks created by LLM applications and autonomous agents, including prompt injection, tool misuse, memory poisoning, vector-store exposure, supply-chain threats, and AI incident response. Every major topic is connected to runnable code, realistic datasets, evaluation discipline, and deployment decisions. Readers learn not only how to build models, but how to test whether results are trustworthy, choose thresholds based on analyst capacity, detect leakage, manage drift, and create evidence that survives review. With case studies, deployment checklists, algorithm cards, dataset cards, exercises, and reproducible workflows, AI Security Engineering turns AI-powered security from an experiment into a defensible engineering practice for modern security teams operating in rapidly changing, adversarial, and increasingly agent-driven digital environments. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: new. Paperback. Most coding interview books teach answers. This book teaches you how to think, communicate, test, and improve under pressure.Data Structures and Algorithms in C++ takes readers from basic loops and functions to interview-ready problem solving through 12 core patterns, 26 focused chapters, nine realistic interview transcripts, and more than 200 practice problems. Every structure is built from first principles in C++17, every major operation includes clear time and space complexity, and every worked example shows not only what to code, but also what to say.Readers learn to recognize recurring problem patterns, choose the right data structure, explain trade-offs, recover from wrong starts, debug with the E-D-G-E method, and connect interview techniques to real production systems. Topics include arrays, linked lists, stacks, queues, hashing, trees, heaps, tries, graphs, greedy algorithms, dynamic programming, bits, and modern C++.With scored transcripts, capstone projects, a readiness test, practical checklists, and a four-week study plan, this book is a complete guide for students, graduates, and professionals preparing to solve confidently, communicate clearly, and perform strongly in technical interviews at leading technology companies worldwide. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: new. Paperback. Most coding interview books teach answers. This book teaches you how to think, communicate, test, and improve under pressure.Data Structures and Algorithms in C++ takes readers from basic loops and functions to interview-ready problem solving through 12 core patterns, 26 focused chapters, nine realistic interview transcripts, and more than 200 practice problems. Every structure is built from first principles in C++17, every major operation includes clear time and space complexity, and every worked example shows not only what to code, but also what to say.Readers learn to recognize recurring problem patterns, choose the right data structure, explain trade-offs, recover from wrong starts, debug with the E-D-G-E method, and connect interview techniques to real production systems. Topics include arrays, linked lists, stacks, queues, hashing, trees, heaps, tries, graphs, greedy algorithms, dynamic programming, bits, and modern C++.With scored transcripts, capstone projects, a readiness test, practical checklists, and a four-week study plan, this book is a complete guide for students, graduates, and professionals preparing to solve confidently, communicate clearly, and perform strongly in technical interviews at leading technology companies worldwide. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condition: new. Paperback. AI Security Engineering is a practical, rigorous guide to building cybersecurity systems that use machine learning, large language models, AI agents, retrieval-augmented generation, MCP, and Python-without sacrificing security, reproducibility, or operational realism. Designed for students, researchers, engineers, and security professionals, the book moves from foundational concepts and intrusion detection to phishing, malware, IoT security, adversarial machine learning, explainability, calibration, federated learning, and production deployment. It also addresses the new risks created by LLM applications and autonomous agents, including prompt injection, tool misuse, memory poisoning, vector-store exposure, supply-chain threats, and AI incident response. Every major topic is connected to runnable code, realistic datasets, evaluation discipline, and deployment decisions. Readers learn not only how to build models, but how to test whether results are trustworthy, choose thresholds based on analyst capacity, detect leakage, manage drift, and create evidence that survives review. With case studies, deployment checklists, algorithm cards, dataset cards, exercises, and reproducible workflows, AI Security Engineering turns AI-powered security from an experiment into a defensible engineering practice for modern security teams operating in rapidly changing, adversarial, and increasingly agent-driven digital environments. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Hardcover. Condition: new. Hardcover. AI Security Engineering is a practical, rigorous guide to building cybersecurity systems that use machine learning, large language models, AI agents, retrieval-augmented generation, MCP, and Python-without sacrificing security, reproducibility, or operational realism. Designed for students, researchers, engineers, and security professionals, the book moves from foundational concepts and intrusion detection to phishing, malware, IoT security, adversarial machine learning, explainability, calibration, federated learning, and production deployment. It also addresses the new risks created by LLM applications and autonomous agents, including prompt injection, tool misuse, memory poisoning, vector-store exposure, supply-chain threats, and AI incident response. Every major topic is connected to runnable code, realistic datasets, evaluation discipline, and deployment decisions. Readers learn not only how to build models, but how to test whether results are trustworthy, choose thresholds based on analyst capacity, detect leakage, manage drift, and create evidence that survives review. With case studies, deployment checklists, algorithm cards, dataset cards, exercises, and reproducible workflows, AI Security Engineering turns AI-powered security from an experiment into a defensible engineering practice for modern security teams operating in rapidly changing, adversarial, and increasingly agent-driven digital environments. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardcover. Condition: new. Hardcover. What if the earliest signs of Parkinson's and Alzheimer's disease are already hidden in the data and machine learning can reveal them before symptoms appear?Signals Before Symptoms is a practical, rigorous guide to building trustworthy machine-learning systems for early-detection research. Written for researchers, engineers, clinicians, and postgraduate students, it explores meaningful signals across MRI, PET, DaTSCAN, speech, gait, wearables, handwriting, digital biomarkers, and clinical, cognitive, and genetic data.Move beyond inflated accuracy. Learn to prevent data leakage, harmonise multisite datasets, manage missing modalities, evaluate performance at real-world prevalence, test explanations, and communicate uncertainty honestly. Twelve revealing case files expose why promising models fail, while a complete worked study demonstrates the journey from research question to defensible conclusion.Featuring exercises, reporting checklists, dataset guidance, code recipes, and global perspectives on regulation, ethics, deployment, and equity, this book helps you create research that is reproducible, clinically meaningful, and built to withstand scrutiny. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardcover. Condition: new. Hardcover. Most coding interview books teach answers. This book teaches you how to think, communicate, test, and improve under pressure.Data Structures and Algorithms in C++ takes readers from basic loops and functions to interview-ready problem solving through 12 core patterns, 26 focused chapters, nine realistic interview transcripts, and more than 200 practice problems. Every structure is built from first principles in C++17, every major operation includes clear time and space complexity, and every worked example shows not only what to code, but also what to say.Readers learn to recognize recurring problem patterns, choose the right data structure, explain trade-offs, recover from wrong starts, debug with the E-D-G-E method, and connect interview techniques to real production systems. Topics include arrays, linked lists, stacks, queues, hashing, trees, heaps, tries, graphs, greedy algorithms, dynamic programming, bits, and modern C++.With scored transcripts, capstone projects, a readiness test, practical checklists, and a four-week study plan, this book is a complete guide for students, graduates, and professionals preparing to solve confidently, communicate clearly, and perform strongly in technical interviews at leading technology companies worldwide. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardcover. Condition: new. Hardcover. What if the earliest signs of Parkinson's and Alzheimer's disease are already hidden in the data and machine learning can reveal them before symptoms appear?Signals Before Symptoms is a practical, rigorous guide to building trustworthy machine-learning systems for early-detection research. Written for researchers, engineers, clinicians, and postgraduate students, it explores meaningful signals across MRI, PET, DaTSCAN, speech, gait, wearables, handwriting, digital biomarkers, and clinical, cognitive, and genetic data.Move beyond inflated accuracy. Learn to prevent data leakage, harmonise multisite datasets, manage missing modalities, evaluate performance at real-world prevalence, test explanations, and communicate uncertainty honestly. Twelve revealing case files expose why promising models fail, while a complete worked study demonstrates the journey from research question to defensible conclusion.Featuring exercises, reporting checklists, dataset guidance, code recipes, and global perspectives on regulation, ethics, deployment, and equity, this book helps you create research that is reproducible, clinically meaningful, and built to withstand scrutiny. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Hardcover. Condition: new. Hardcover. Most coding interview books teach answers. This book teaches you how to think, communicate, test, and improve under pressure.Data Structures and Algorithms in C++ takes readers from basic loops and functions to interview-ready problem solving through 12 core patterns, 26 focused chapters, nine realistic interview transcripts, and more than 200 practice problems. Every structure is built from first principles in C++17, every major operation includes clear time and space complexity, and every worked example shows not only what to code, but also what to say.Readers learn to recognize recurring problem patterns, choose the right data structure, explain trade-offs, recover from wrong starts, debug with the E-D-G-E method, and connect interview techniques to real production systems. Topics include arrays, linked lists, stacks, queues, hashing, trees, heaps, tries, graphs, greedy algorithms, dynamic programming, bits, and modern C++.With scored transcripts, capstone projects, a readiness test, practical checklists, and a four-week study plan, this book is a complete guide for students, graduates, and professionals preparing to solve confidently, communicate clearly, and perform strongly in technical interviews at leading technology companies worldwide. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Hardcover. Condition: new. Hardcover. AI Security Engineering is a practical, rigorous guide to building cybersecurity systems that use machine learning, large language models, AI agents, retrieval-augmented generation, MCP, and Python-without sacrificing security, reproducibility, or operational realism. Designed for students, researchers, engineers, and security professionals, the book moves from foundational concepts and intrusion detection to phishing, malware, IoT security, adversarial machine learning, explainability, calibration, federated learning, and production deployment. It also addresses the new risks created by LLM applications and autonomous agents, including prompt injection, tool misuse, memory poisoning, vector-store exposure, supply-chain threats, and AI incident response. Every major topic is connected to runnable code, realistic datasets, evaluation discipline, and deployment decisions. Readers learn not only how to build models, but how to test whether results are trustworthy, choose thresholds based on analyst capacity, detect leakage, manage drift, and create evidence that survives review. With case studies, deployment checklists, algorithm cards, dataset cards, exercises, and reproducible workflows, AI Security Engineering turns AI-powered security from an experiment into a defensible engineering practice for modern security teams operating in rapidly changing, adversarial, and increasingly agent-driven digital environments. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…