Search results for "probability for deep learning quantum"
Items we need to order for you
Proceedings of 17th International Conference on Machine Learning and Computing
This book comprises original and peer reviewed research papers presented at 2025 17th International Conference on Machine Learning and Computing that was held in Guangzhou, China, from February 14 to 17, 2025. The focus of the conference is to establish an effective platform for institutions and industries to share ideas and to present the works of scientists, engineers, educators and students from all over the world. Topics discussed in this volume include Machine Learning Theory and Algorithms, High-performance Computing Models and Data Processing, Large-scale Language Models and Natural Language Processing, Data-oriented Information System Optimization and Intelligent Computing, AI-based Intelligent Control Systems and System Security, etc. The book will become a valuable resource for academics, industry professionals, and engineers working in the related fields of machine learning and computing.
R 8,059.00
Mathematical Foundations of Reinforcement Learning
This book provides a mathematical yet accessible introduction to the fundamental concepts, core challenges, and classic reinforcement learning algorithms. It aims to help readers understand the theoretical foundations of algorithms, providing insights into their design and functionality. Numerous illustrative examples are included throughout. The mathematical content is carefully structured to ensure readability and approachability.The book is divided into two parts. The first part is on the mathematical foundations of reinforcement learning, covering topics such as the Bellman equation, Bellman optimality equation, and stochastic approximation. The second part explicates reinforcement learning algorithms, including value iteration and policy iteration, Monte Carlo methods, temporal-difference methods, value function methods, policy gradient methods, and actor-critic methods.With its comprehensive scope, the book will appeal to undergraduate and graduate students, post-doctoral researchers, lecturers, industrial researchers, and anyone interested in reinforcement learning.
R 3,663.00
Agentic Hyper-Personalized Dimensions
Agentic Hyper-Personalized Dimensions - Six Dimensions of Business Dark Data explores how generative AI (Gen-AI), combined with agentic intelligence, can transform raw enterprise data into hyper-personalized, actionable insights. This book introduces a practical framework for building AI-powered agent swarms that operate across six critical dimensions of business dark data, empowering intelligent automation, decision augmentation, and strategic foresight at unprecedented scale. In an age when data is underutilized, organizations are overwhelmed by complexity, latency, and fragmentation. Traditional analytics pipelines fall short in handling dynamic environments and human-centric demands. This book addresses this critical gap by detailing how autonomous Gen-AI agents -- built on foundational disciplines of data engineering, data science, and machine learning -- can process, personalize, and operationalize hidden insights buried deep within enterprise systems. The six dimensions presented in the book form a cognitive and computational blueprint that guides the design, deployment, and evolution of agentic swarms. Each dimension focuses on a specific mode of intelligence -- such as contextual reasoning, emotional alignment, or real-time adaptive sensing -- and shows how AI agents can specialize within each domain to maximize business impact. Readers won't just learn theory -- they'll explore field-tested methodologies to deploy Gen-AI-powered solutions at enterprise scale. From zoned data lakes and transformer models to swarm governance and consensus mechanisms, the book walks readers through the full life cycle of building intelligent systems that learn, evolve, and act with purpose. What makes this book essential -- and unlike others in the market -- is that the author is not writing from the sidelines. He is actively architecting and delivering these systems today, with firsthand insight into what works, what fails, and what the future holds. The methodology presented blends system design, human-aligned AI, industrial automation, and trusted decision-making, offering readers a rich and practical road map for AI-first enterprise innovation. What You Will Learn How to structure AI-powered agents across six interlinked cognitive and computational dimensions to effectively process dark data and deliver hyper-personalized, real-time business insights.Practical knowledge on how to orchestrate swarms of Gen-AI agents -- governed by Councils -- that collaboratively manage complexity, detect emerging patterns, and operate autonomously in enterprise environments.The foundational and advanced skills required to implement generative AI and agent-based intelligence within large-scale, multi-cloud, and hybrid business ecosystems. Who This Book is For This book is designed for professionals and practitioners operating at the intersection of artificial intelligence and business transformation. It is ideal for AI developers, data scientists, solution architects, e
R 1,368.00
Guide to Implementing MLOps
Over the past decade, machine learning has come a long way, with organisations of all sizes exploring its potential to extract valuable insights from data. However, despite the promise of machine learning, many organisations need help deploying and managing machine learning models in production. This is where MLOps comes in. MLOps, or machine learning operations, is an emerging field that focuses on the deployment, management, and monitoring of machine learning models in production environments. MLOps combines the principles of DevOps with the unique requirements of machine learning, enabling organisations to build and deploy models at scale while maintaining high levels of reliability and accuracy. This book is a comprehensive guide to MLOps, providing readers with a deep understanding of the principles, best practices, and emerging trends in the field. From training models to deploying them in production, the book covers all aspects of the MLOps process, providing readers with the knowledge and tools they need to implement MLOps in their organisations. The book is aimed at data scientists, machine learning engineers, and IT professionals who are interested in deploying machine learning models at scale. It assumes a basic understanding of machine learning concepts and programming, but no prior knowledge of MLOps is required. Whether you're just getting started with MLOps or looking to enhance your existing knowledge, this book is an essential resource for anyone interested in scaling machine learning in production.
R 1,832.00
