Math and Artificial Intelligence (49 risultati)

Paperback. Condizione: New.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: As New. Unread book in perfect condition.

Condizione: As New. Unread book in perfect condition.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

Condizione: New.

Paperback. Condizione: new. Paperback. In today's interconnected digital society, the protection of information is not just a technical requirement but a cornerstone of trust, governance, and progress. Every email we send, every financial transaction we make, every online search query we type, and every AI system that analyzes data in real time-all depend on cryptographic security. Cryptography, once regarded as a highly specialized tool of spies, mathematicians, and military strategists, has now become a pervasive and indispensable technology that underpins the very infrastructure of our global digital economy.At the heart of cryptography lies number theory-a branch of pure mathematics that investigates the properties of integers, prime numbers, modular arithmetic, and abstract algebraic structures. What was once considered "the queen of mathematics" and "an art for art's sake" has become one of the most powerful weapons against cybercrime. Techniques such as modular exponentiation, prime testing, elliptic curve arithmetic, and the discrete logarithm problem are not only abstract mathematical curiosities but also the lifeblood of secure communication, online banking, e-commerce, blockchain, and digital identity.However, the story does not stop at cryptography and number theory. The dawn of the Artificial Intelligence (AI) era has added a new layer of complexity and opportunity to the field of security. AI has empowered cybersecurity systems with the ability to detect anomalies, predict attacks, defend against malware, and even break weak cryptographic algorithms. But at the same time, AI introduces new threats-adversarial AI attacks, data poisoning, model inversion, and large-scale misinformation campaigns-all of which demand new security paradigms that combine mathematics, algorithms, and intelligent defense mechanisms.This book, "Cryptography, Number Theory, and AI Security: Foundations and Applications", is designed as a bridge between classical mathematical security principles and modern AI-driven defense strategies. It offers a comprehensive exploration of the fundamentals of cryptography, the mathematical structures underlying secure systems, and the emerging role of AI in both defending and attacking these systems.Why This Book?The motivation behind this book is rooted in the urgent necessity to educate the next generation of cybersecurity professionals, mathematicians, and AI researchers about the convergence of three critical domains: Cryptography - The science of securing communication.Number Theory - The mathematical foundation of modern encryption.AI Security - The defense and offense mechanism in the age of intelligent cyberattacks.Most books in the current literature focus on one of these domains in isolation. Cryptography textbooks tend to emphasize classical and modern ciphers but often neglect the growing influence of AI. Number theory texts explore mathematical elegance but seldom address real-world cryptographic implementations. AI security resources focus heavily on intrusion detection and anomaly detection but rarely link back to the mathematical principles of encryption and secure computation.This book is different. It offers a holistic approach: It teaches number theory not as an abstract subject but as a tool for cryptography.It explains cryptography not just as a system of locks and keys but as a living technology facing AI-driven adversaries.It highlights AI security not just as a buzzword but as a mathematically grounded, cryptographically informed discipline.By unifying these perspectives, the book provides students, researchers, and professionals with a robust conceptual and practical toolkit that can be applie Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: New.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condizione: New.

Paperback. Condizione: new. Paperback. The 21st century has been defined by data - massive, continuous, and omnipresent. Every human action, every digital interaction, every business transaction generates data. This overwhelming flood of data, commonly referred to as Big Data, has transformed how we perceive the world, how businesses operate, and how decisions are made. However, Big Data alone is meaningless without the mathematical tools, scalable algorithms, and computational frameworks that allow us to transform raw data into actionable intelligence.This book, "Big Data Mathematics and AI Algorithms: Foundations of Scalable Machine Learning," has been written to bridge a critical gap in the understanding of the mathematical foundations of Big Data and their application in Artificial Intelligence (AI). While countless books exist on AI, machine learning, and data science, very few focus specifically on the mathematical rigor required to handle large-scale datasets and parallel computation.The main purpose of this book is to equip students, researchers, and professionals with both the theoretical understanding and practical insights needed to design, implement, and scale AI algorithms for Big Data applications. It does not simply teach machine learning from a traditional perspective; it shows how mathematics makes large-scale AI possible, how parallel computations make it efficient, and how scalable algorithms make it relevant in real-world scenarios. Why This Book is ImportantThe world is moving toward data-driven intelligence at an unprecedented scale. From healthcare systems processing petabytes of patient records, to e-commerce platforms recommending products to billions of users, to self-driving cars processing terabytes of sensor data per hour - mathematics is the silent engine that powers every computation.This book is important because it: Brings Mathematical Clarity to Big Data and AI: Students often learn AI algorithms as "recipes" - plug in data, run code, get results - but fail to understand the mathematical reasoning behind why an algorithm works, how it converges, and how it scales. This book explains the linear algebra, probability, and optimization foundations that power scalable AI models.Focuses on Scalability: Traditional machine learning works fine on small datasets, but Big Data demands scalable solutions. This book explains distributed versions of common algorithms, teaches readers how to parallelize computations, and introduces tools like MapReduce, Spark, and GPU-based training.Builds Practical Competence: Every chapter connects theory to practice with examples, case studies, and computational strategies. This helps readers move from understanding equations to implementing them efficiently.Prepares for Research and Industry: The book is particularly valuable for UGC NET aspirants, graduate students, data scientists, and AI engineers, as it combines deep theory with industry-level frameworks. How This Book is StructuredThis book is divided into ten carefully crafted chapters, each designed to progressively develop the reader's understanding.Chapter 1 introduces the landscape of Big Data and AI, explaining the need for scalable algorithms.Chapter 2 builds the linear algebra foundation, which is essential for matrix operations, dimensionality reduction, and data transformations.Chapter 3 covers probability, statistics, and stochastic processes, which are at the heart of AI models.Chapter 4 teaches optimization techniques that enable model training, including distributed optimiza Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

Paperback. Condizione: new. Paperback. In the rapidly growing field of artificial intelligence (AI) and machine learning (ML), the role of mathematics-particularly linear algebra and matrix computations-cannot be overstated. Every neural network, from the simplest perceptron to the most advanced convolutional neural network (CNN) or transformer model, is fundamentally built upon matrix and tensor operations. While researchers and engineers often interact with these operations indirectly through deep learning frameworks such as TensorFlow, PyTorch, or JAX, the efficiency, interpretability, and scalability of these systems depend directly on a deep understanding of matrix computations.The book "Matrix Computations for Deep Learning" is written with the goal of bridging the gap between the theoretical foundations of matrix algebra and the applied techniques in deep learning. By focusing on singular value decomposition (SVD), tensor operations, and convolutional neural network foundations, this book provides students, researchers, and industry professionals with both the conceptual clarity and the practical skills necessary to design, implement, and optimize modern AI systems. Why This Book is NeededIn most existing textbooks on deep learning, matrix computations are introduced briefly as a background requirement, often summarized in one or two introductory chapters. While this approach may provide enough to begin coding neural networks, it leaves a gap in understanding how these computations actually shape model performance, stability, and scalability.For example: Singular Value Decomposition (SVD) is not just a mathematical trick; it is at the heart of data compression, dimensionality reduction, and optimization in deep learning.Tensor decompositions are not merely advanced algebraic tools; they enable model compression, multi-modal learning, and scalable architectures for big data.Convolutions, the backbone of CNNs, are more than a "sliding filter" - they can be fully understood as structured matrix multiplications that connect directly to Fourier transforms and wavelets.This book is therefore not just about theory or coding, but about creating a deep mathematical intuition while always keeping in mind the practical applications in deep learning. How This Book is StructuredThe book is divided into six major parts: Foundations of Matrix Computations - covering linear algebra basics, vector spaces, and norms that are directly applied in neural network optimization.Matrix Decompositions - exploring SVD, QR, LU, and eigenvalue decompositions with applications in dimensionality reduction, regularization, and optimization.Tensor Operations - moving beyond matrices to higher-order tensors, tensor decompositions, and computational efficiency in frameworks like PyTorch and TensorFlow.Matrix Computations for CNNs - showing how convolutions, pooling, and backpropagation can be represented entirely through structured matrix operations.Applications and Advanced Topics - linking matrix methods with dimensionality reduction, computer vision, and large-scale AI systems.Practical Implementations - providing hands-on coding examples in Python, with an emphasis on efficiency, stability, and scalability.Each chapter contains mathematical explanations, graphical illustrations, step-by-step derivations, and code snippets, ensuring that readers not only understand the concepts but also see how they are implemented in practice. Why This Book is Important for Study1. Building Mathematical Intuition for Deep LearningMatrix computations are the foundation upon which deep learning is built. Without a solid grasp of these operat Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condizione: New.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condizione: New.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.