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ENGLISH

Embodied Multi-Agent Systems - Perception, Action, And Learning

by Di Guo, Huaping Liu, Kangyao Huang e Xinzhu Liu

~~211,34€~~ 190,21€ 10% OFF

 While existing books address collaboration control, perception, and learning, there is a gap in focusing on active perception and interactive learning for embodied multi-agent systems.
ENGLISH

Cross-Device Federated Recommendation - Privacy-Preserving Personalization

by Guojiang Shen, Lingyun Wang, Mengmeng Wang e Xiangjie Kong

~~145,09€~~ 130,58€ 10% OFF

 This book introduces the prevailing domains of recommender systems and cross-device federated learning, highlighting the latest research progress and prospects regarding cross-device federated recommendation.
ENGLISH

Foundations Of Deep Learning

by Dacheng Tao e Fengxiang He

~~158,34€~~ 142,51€ 10% OFF

 The excellent generalizability of deep learning is like a â€œcloudâ€ to conventional complexity-based learning theory: the over-parameterization of deep learning makes almost all existing tools vacuous.
ENGLISH

Unsupervised Domain Adaptation - Recent Advances And Future Perspectives

by Jingjing Li, Lei Zhu e Zhekai Du

~~198,74€~~ 178,87€ 10% OFF

 Unsupervised domain adaptation (UDA) is a challenging problem in machine learning where the model is trained on a source domain with labeled data and tested on a target domain with unlabeled data. In recent years, UDA has received significant attention from the research community due to its applicability in various real-world scenarios. This book provides a comprehensive review of state-of-the-art UDA methods and explores new variants of UDA that have the potential to advance the field.The book begins with a clear introduction to the UDA problem and is mainly organized into four technical sections, each focused on a specific piece of UDA research. The first section covers criterion optimization-based UDA, which aims to learn domain-invariant representations by minimizing the discrepancy between source and target domains. The second section discusses bi-classifier adversarial learning-based UDA, which creatively leverages adversarial learning by conducting a minimax game between the feature extractor and two task classifiers. The third section introduces source-free UDA, a novel UDA setting that does not require any raw data from the source domain. The fourth section presents active learning for UDA, which combines domain adaptation and active learning to reduce the amount of labeled data needed for adaptation.This book is suitable for researchers, graduate students, and practitioners who are interested in UDA and its applications in various fields, primarily in computer vision. The chapters are authored by leading experts in the field and provide a comprehensive and in-depth analysis of the current UDA methods and new directions for future research. With its broad coverage and cutting-edge research, this book is a valuable resource for anyone looking to advance their knowledge of UDA.
ENGLISH

Robust Machine Learning - Distributed Methods For Safe Ai

by Nirupam Gupta, Rachid Guerraoui e Rafael Pinot

~~158,34€~~ 142,51€ 10% OFF

 Today, machine learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may misbehave and jeopardize the learning procedure. This could, for example, result from hardware and software bugs, data poisoning or a malicious player controlling a subset of the machines. This book explains in simple terms what it means for a distributed machine learning scheme to be robust to these threats, and how to build provably robust machine learning algorithms.Studying the robustness of machine learning algorithms is a necessity given the ubiquity of these algorithms in both the private and public sectors. Accordingly, over the past few years, we have witnessed a rapid growth in the number of articles published on the robustness of distributed machine learning algorithms. We believe it is time to provide a clear foundation to this emerging and dynamic field. By gathering the existing knowledge and democratizing the concept of robustness, the book provides the basis for a new generation of reliable and safe machine learning schemes. In addition to introducing the problem of robustness in modern machine learning algorithms, the book will equip readers with essential skills for designing distributed learning algorithms with enhanced robustness. Moreover, the book provides a foundation for future research in this area.
ENGLISH

Online Machine Learning - A Practical Guide With Examples In Python

~~72,86€~~ 65,57€ 10% OFF

 This book deals with the exciting, seminal topic of Online Machine Learning (OML). The content is divided into three parts: the first part looks in detail at the theoretical foundations of OML, comparing it to Batch Machine Learning (BML) and discussing what criteria should be developed for a meaningful comparison. The second part provides practical considerations, and the third part substantiates them with concrete practical applications.The book is equally suitable as a reference manual for experts dealing with OML, as a textbook for beginners who want to deal with OML, and as a scientific publication for scientists dealing with OML since it reflects the latest state of research. But it can also serve as quasi OML consulting since decision-makers and practitioners can use the explanations to tailor OML to their needs and use it for their application and ask whether the benefits of OML might outweigh the costs.OML will soon become practical; it is worthwhile to get involved with it now. This book already presents some tools that will facilitate the practice of OML in the future. A promising breakthrough is expected because practice shows that due to the large amounts of data that accumulate, the previous BML is no longer sufficient. OML is the solution to evaluate and process data streams in real-time and deliver results that are relevant for practice.In addition to this book, interactive Jupyter Notebooks and further material about OML are provided in the GitHub repository (https://github.com/sn-code-inside/online-machine-learning). The repository is continuously maintained, so the notebooks may change over time.
ENGLISH

Artificial Intelligence In Business Management

by Teik Toe Teoh e Yu Jin Goh

~~72,86€~~ 65,57€ 10% OFF

 Artificial intelligence (AI) is rapidly gaining significance in the business world. With more and more organizations adopt AI technologies, there is a growing demand for business leaders, managers, and practitioners who can harness AI’s potential to improve operations, increase efficiency, and drive innovation.This book aims to help management professionals exploit the predictive powers of AI and demonstrate to AI practitioners how to apply their expertise in fundamental business operations. It showcases how AI technology innovations can enhance various aspects of business management, such as business strategy, finance, and marketing. Readers interested in AI for business management will find several topics of particular interest, including how AI can improve decision-making in business strategy, streamline operational processes, and enhance customer satisfaction.As AI becomes an increasingly important tool in the business world, this book offers valuable insightsinto how it can be applied to various industries and business settings. Through this book, readers will gain a better understanding of how AI can be applied to improve business management practices and practical guidance on how to implement AI projects in a business context. This book also provides practical guides on how to implement AI projects in a business context using Python programming. By reading this book, readers will be better equipped to make informed decisions about how to leverage AI for business success.
ENGLISH

Introduction To Transfer Learning - Algorithms And Practice

by Jindong Wang e Yiqiang Chen

59,61€ + 10% CARD

 Transfer learning is one of the most important technologies in the era of artificial intelligence and deep learning. It seeks to leverage existing knowledge by transferring it to another, new domain. Over the years, a number of relevant topics have attracted the interest of the research and application community: transfer learning, pre-training and fine-tuning, domain adaptation, domain generalization, and meta-learning. This book offers a comprehensive tutorial on an overview of transfer learning, introducing new researchers in this area to both classic and more recent algorithms. Most importantly, it takes a "student’s" perspective to introduce all the concepts, theories, algorithms, and applications, allowing readers to quickly and easily enter this area. Accompanying the book, detailed code implementations are provided to better illustrate the core ideas of several important algorithms, presenting good examples for practice.
ENGLISH

Evolutionary Multi-Task Optimization - Foundations And Methodologies

by Abhishek Gupta, Kay Chen Tan, Liang Feng e Yew Soon Ong

198,09€ + 10% CARD

 In contrast to traditional evolutionary searches, which solve a single task in a single run, evolutionary multi-tasking algorithm conducts searches concurrently on multiple search spaces corresponding to different tasks or optimization problems,each possessing a unique function landscape.
ENGLISH

Federated Learning - Fundamentals And Advances

by Hangyu Zhu, Jinjin Xu, Yang Chen e Yaochu Jin

185,49€ + 10% CARD

 This book introduces readers to the fundamentals of and recent advances in federated learning, focusing on reducing communication costs, improving computational efficiency, and enhancing the security level.
ENGLISH

Artificial Intelligence With Python

by Teik Toe Teoh e Zheng Rong

59,61€ + 10% CARD

 Entering the field of artificial intelligence and data science can seem daunting to beginners with little to no prior background, especially those with no programming experience.
ENGLISH

Artificial Intelligence With Python

by Teik Toe Teoh e Zheng Rong

81,10€ + 10% CARD

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 Entering the field of artificial intelligence and data science can seem daunting to beginners with little to no prior background, especially those with no programming experience.
ENGLISH

Machine Learning - The Basics

by Alexander Jung

72,86€ + 10% CARD

 The book trains readers to break down various ML applications and methods in terms of data, model, and loss, thus helping them to choose from the vast range of ready-made ML methods. The bookâ€™s three-component approach to ML provides uniform coverage of a wide range of concepts and techniques.
ENGLISH

Genetic Programming For Production Scheduling - An Evolutionary Learning Approach

by Fangfang Zhang, Mengjie Zhang, Su Nguyen e Yi Mei

171,59€ + 10% CARD

 This book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. In Part I, it provides an introduction to production scheduling, existing solution methods, and the GP approach to production scheduling.
ENGLISH

Machine Learning With Julia - An Algorithmic Exploration

by Jeremiah D. Deng

~~81,10€~~ 72,99€ 10% OFF

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