Search results for "Data Learning Press"
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Computational Learning Theory
Computational learning theory is a subject which has been advancing rapidly in the last few years. The authors concentrate on the probably approximately correct model of learning, and gradually develop the ideas of efficiency considerations. Finally, applications of the theory to artificial neural networks are considered. Many exercises are included throughout, and the list of references is extensive. This volume is relatively self contained as the necessary background material from logic, probability and complexity theory is included. It will therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics.
R 2,306.74
Algebraic Geometry and Statistical Learning Theory
Sure to be influential, this book lays the foundations for the use of algebraic geometry in statistical learning theory. Many widely used statistical models and learning machines applied to information science have a parameter space that is singular: mixture models, neural networks, HMMs, Bayesian networks, and stochastic context-free grammars are major examples. Algebraic geometry and singularity theory provide the necessary tools for studying such non-smooth models. Four main formulas are established: 1. the log likelihood function can be given a common standard form using resolution of singularities, even applied to more complex models; 2. the asymptotic behaviour of the marginal likelihood or 'the evidence' is derived based on zeta function theory; 3. new methods are derived to estimate the generalization errors in Bayes and Gibbs estimations from training errors; 4. the generalization errors of maximum likelihood and a posteriori methods are clarified by empirical process theory on algebraic varieties.
R 4,120.04
Tiny Machine Learning: Design Principles and Applications
An expert compilation of on-device training techniques, regulatory frameworks, and ethical considerations of TinyML design and development In Tiny Machine Learning: Design Principles and Applications, a team of distinguished researchers delivers a comprehensive discussion of the critical concepts, design principles, applications, and relevant issues in Tiny Machine Learning (TinyML). Expert contributors introduce a new low power resource, offering vast applications in IoT devices with system-algorithm co-design. Tiny Machine Learning explores TinyML paradigms and enablers, TinyML for anomaly detection, and the learning panorama under TinyML. Readers will find explanations of TinyML devices and tools, power consumption and memory in IoT microcontrollers, and lightweight frameworks for TinyML. The book also describes TinyML techniques for real-time and environmental applications. Additional topics covered in the book include: A thorough introduction to security and privacy techniques for TinyML devices, including the implementation of novel security schemesIncisive explorations of power consumption and memory in IoT MCUs, including ultralow-power smart IoT devices with embedded TinyMLPractical discussions of TinyML research targeting microcontrollers for data extraction and synthesis Perfect for industry and academic researchers, scientists, and engineers, Tiny Machine Learning will also benefit lecturers and graduate students interested in machine learning.
R 4,559.00
Derivative-Free Optimization
This book offers a pioneering exploration of classification-based derivative-free optimization (DFO), providing researchers and professionals in artificial intelligence, machine learning, AutoML, and optimization with a robust framework for addressing complex, large-scale problems where gradients are unavailable. By bridging theoretical foundations with practical implementations, it fills critical gaps in the field, making it an indispensable resource for both academic and industrial audiences. The book introduces innovative frameworks such as sampling-and-classification (SAC) and sampling-and-learning (SAL), which underpin cutting-edge algorithms like Racos and SRacos. These methods are designed to excel in challenging optimization scenarios, including high-dimensional search spaces, noisy environments, and parallel computing. A dedicated section on the ZOOpt toolbox provides practical tools for implementing these algorithms effectively. The book's structure moves from foundational principles and algorithmic development to advanced topics and real-world applications, such as hyperparameter tuning, neural architecture search, and algorithm selection in AutoML. Readers will benefit from a comprehensive yet concise presentation of modern DFO methods, gaining theoretical insights and practical tools to enhance their research and problem-solving capabilities. A foundational understanding of machine learning, probability theory, and algorithms is recommended for readers to fully engage with the material.
R 6,996.69
Machine Learning and Soft Computing
This two part-volume CCIS constitutes the refereed proceedings of 9th International Conference, ICMLSC 2025, in Tokyo, Japan in January 24–26, 2025. The 39 full papers and 13 short papers included in this book were carefully reviewed and selected from 121 submissions. They follow the topical sections as below:Part I : Multimodal Data Analysis and Model Optimization; Basic Theories of Machine Learning and Emerging Application Technologies; and Intelligent Recommendation System Design and Privacy Security.Part II : Deep Learning Models and High-performance Computing; Data-driven Complex System Modeling and Intelligent Optimization Algorithms; and Image Analysis and Processing Methods based on AI.
R 3,895.87
Methodologies, Frameworks, and Applications of Machine Learning
Technology is constantly evolving, and machine learning is positioned to become a pivotal tool with the power to transform industries and revolutionize everyday life. This book underscores the urgency of leveraging the latest machine learning methodologies and theoretical advancements, all while harnessing a wealth of realistic data and affordable computational resources. Machine learning is no longer confined to theoretical domains; it is now a vital component in healthcare, manufacturing, education, finance, law enforcement, and marketing, ushering in an era of data-driven decision-making. Academic scholars seeking to unlock the potential of machine learning in the context of Industry 5.0 and advanced IoT applications will find that the groundbreaking book, Methodologies, Frameworks, and Applications of Machine Learning , introduces an unmissable opportunity to delve into the forefront of modern research and application. This book offers a wealth of knowledge and practical insights across a wide array of topics, ranging from conceptual frameworks and methodological approaches to the application of probability theory, statistical techniques, and machine learning in domains as diverse as e-government, healthcare, cyber-physical systems, and sustainable development, this comprehensive guide equips you with the tools to navigate the complexities of Industry 5.0 and the Internet of Things (IoT). Students, researchers, and practitioners will find that this book serves as a beacon of wisdom, guiding towards the future of machine learning and its innumerable applications. Harness the power of machine learning to address real-world challenges and transform the way we perceive and engage with the world.
R 11,565.64
Artificial Intelligence
This book presents contextualised and detailed research on Artificial Intelligence (AI) and the Global South. It examines the key challenges of these emerging and least industrialised countries while proffering holistic and comprehensive solutions. The book then explains how AI, as part of these broad interventions, can drive Global South economies to achieve inclusive development and shared prosperity. The book outlines how countries can swiftly prepare to adopt and develop AI across all sectors. It presents novel national, regional, and continental AI adoption, development, and implementation frameworks.Features:Broad non-AI interventions and prescriptions to address Global South challengesA comprehensive but accessible introduction to AI concepts, technology, infrastructure, systems, and innovations such as AlphaFold, ChatGPT-4, and DeepSeek-R1An overview of AI-related technologies such as quantum computing, battery energy storage systems, 3D printing, nanotechnology, IoT, and blockchainHow to prepare emerging economies to unlock the benefits of AI while mitigating the risksDiscussion of specific AI applications in 11 critical Global South sectorsDetails of 11 sector case studies of AI adoption in the Global South and Global NorthTen country case studies: Sharing emergent AI experiences in the Global SouthAI adoption framework: vision, strategy, policy, governance, legislation/regulation, and implementation matrixA framework for democratising and decolonising AIThe value proposition for AI research, development, and ownership in the Global SouthA case for the participation of the Global South in the AI semiconductor industryThis book is aimed at policymakers, business leaders, graduate students, academics, researchers, strategic thinkers, and world leaders seeking to understand and leverage the transformative role of AI-based systems in achieving inclusive development, economic transformation, and shared prosperity.
R 2,711.00
R 333.41
