Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, 122,
Автор: Rosendo Abellera; Lakshman Bulusu Название: Oracle Business Intelligence with Machine Learning ISBN: 1484232542 ISBN-13(EAN): 9781484232545 Издательство: Springer Рейтинг: Цена: 5309.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Use machine learning and Oracle Business Intelligence Enterprise Edition (OBIEE) as a comprehensive BI solution. This book follows a when-to, why-to, and how-to approach to explain the key steps involved in utilizing the artificial intelligence components now available for a successful OBIEE implementation. Oracle Business Intelligence with Machine Learning covers various technologies including using Oracle OBIEE, R Enterprise, Spatial Maps, and machine learning for advanced visualization and analytics. The machine learning material focuses on learning representations of input data suitable for a given prediction problem. This book focuses on the practical aspects of implementing machine learning solutions using the rich Oracle BI ecosystem. The primary objective of this book is to bridge the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to machine learning with OBIEE. What You Will Learn
See machine learning in OBIEE
Master the fundamentals of machine learning and how it pertains to BI and advanced analytics
Gain an introduction to Oracle R Enterprise
Discover the practical considerations of implementing machine learning with OBIEE
Who This Book Is ForAnalytics managers, BI architects and developers, and data scientists.
Автор: Annalisa Appice; Pedro Pereira Rodrigues; V?tor Sa Название: Machine Learning and Knowledge Discovery in Databases ISBN: 3319235249 ISBN-13(EAN): 9783319235240 Издательство: Springer Рейтинг: Цена: 12298.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers.
Автор: Tapio Elomaa; Heikki Mannila; Hannu Toivonen Название: Machine Learning: ECML 2002 ISBN: 3540440364 ISBN-13(EAN): 9783540440369 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Constituting the preceedings of the 13th European Conference on Machine Learning, these papers cover topics such as: computational discovery; search strategies; classification; support vector machines; kernel methods; rule induction; linear learning; decision tree learning; and boosting.
Автор: Perez Castano, Arnaldo Название: Practical artificial intelligence ISBN: 1484233565 ISBN-13(EAN): 9781484233566 Издательство: Springer Рейтинг: Цена: 11878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Discover how all levels Artificial Intelligence (AI) can be present in the most unimaginable scenarios of ordinary lives. This book explores subjects such as neural networks, agents, multi agent systems, supervised learning, and unsupervised learning. These and other topics will be addressed with real world examples, so you can learn fundamental concepts with AI solutions and apply them to your own projects. People tend to talk about AI as something mystical and unrelated to their ordinary life. Practical Artificial Intelligence provides simple explanations and hands on instructions. Rather than focusing on theory and overly scientific language, this book will enable practitioners of all levels to not only learn about AI but implement its practical uses. What You'll Learn
Understand agents and multi agents and how they are incorporated
Relate machine learning to real-world problems and see what it means to you
Apply supervised and unsupervised learning techniques and methods in the real world
Implement reinforcement learning, game programming, simulation, and neural networks
Who This Book Is For Computer science students, professionals, and hobbyists interested in AI and its applications.
Автор: Gogate & Hollich Название: Theoretical And Computational Models Of Word Learning ISBN: 1466629738 ISBN-13(EAN): 9781466629738 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 25502.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The process of learning words and languages may seem like an instinctual trait, inherent to nearly all humans from a young age. However, a vast range of complex research and information exists in detailing the complexities of the process of word learning. <br><br><em>Theoretical and Computational Models of Word Learning: Trends in Psychology and Artificial Intelligence</em> strives to combine cross-disciplinary research into one comprehensive volume to help readers gain a fuller understanding of the developmental processes and influences that makeup the progression of word learning. Blending together developmental psychology and artificial intelligence, this publication is intended for researchers, practitioners, and educators who are interested in language learning and its development as well as computational models formed from these specific areas of research.
Автор: Ceci Название: Machine Learning and Knowledge Discovery in Databases ISBN: 3319712489 ISBN-13(EAN): 9783319712482 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The three volume proceedings LNAI 10534 - 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017.
Описание: The integration of logic and probability combines the capability of the first to represent complex relations among entities with the capability of the latter to model uncertainty over attributes and relations. Logic programming provides a Turing complete language based on logic and thus represent an excellent candidate for the integration.Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. One of most successful approaches to Probabilistic Logic Programming is the Distribution Semantics, where a probabilistic logic program defines a probability distribution over normal logic programs and the probability of a ground query is then obtained from the joint distribution of the query and the programs. Foundations of Probabilistic Logic Programming aims at providing an overview of the field of Probabilistic Logic Programming, with a special emphasis on languages under the Distribution Semantics. The book presents the main ideas for semantics, inference and learning and highlights connections between the methods.Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online.
This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories, trends, challenges, and applications. More than a third of this edition comprised of new materials, highlighting descriptions of the classic methods, and extensions and novel approaches that have recently been introduced.
Along with algorithmic descriptions of each method, the settings in which each method is applicable and the consequences and tradeoffs incurred by using the method is succinctly featured. R code for implementation of the algorithm is also emphasized.
The unique volume provides researchers, students and practitioners in industry with a comprehensive, concise and convenient resource on ensemble learning methods.
Автор: Siddhartha Bhattacharyya, Indrajit Pan, Ashish Mani, Sourav De, Elizabeth Behrman, Susanta Chakraborti Название: Quantum Machine Learning ISBN: 311067064X ISBN-13(EAN): 9783110670646 Издательство: Walter de Gruyter Цена: 20446.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving a classical machine learning method. Such algorithms typically require one to encode the given classical dataset into a quantum computer, so as to make it accessible for quantum information processing. After this, quantum information processing routines can be applied and the result of the quantum computation is read out by measuring the quantum system. For example, the outcome of the measurement of a qubit could reveal the result of a binary classification task. While many proposals of quantum machine learning algorithms are still purely theoretical and require a full-scale universal quantum computer to be tested, others have been implemented on small-scale or special purpose quantum devices. The salient features of the book include: In depth analysis of the subject matter with mathematical discourse Video demonstration of each chapter for enabling the readers to have a good understanding of the chapter contents. Examples on real life applications. Illustrative diagrams Coding examples
Автор: J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant Название: Deep Learning Techniques and Optimization Strategies in Big Data Analytics ISBN: 179981193X ISBN-13(EAN): 9781799811930 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 27027.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
Автор: Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, Han Yu Название: Federated Learning ISBN: 1681736977 ISBN-13(EAN): 9781681736976 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 12335.00 р. Наличие на складе: Нет в наличии.
Описание: How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.
Автор: Matthias Boehm, Arun Kumar, Jun Yang Название: Data Management in Machine Learning Systems ISBN: 1681734982 ISBN-13(EAN): 9781681734989 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 13167.00 р. Наличие на складе: Нет в наличии.
Описание: Large-scale data analytics using machine learning (ML) underpins many modern data-driven applications. ML systems provide means of specifying and executing these ML workloads in an efficient and scalable manner. Data management is at the heart of many ML systems due to data-driven application characteristics, data-centric workload characteristics, and system architectures inspired by classical data management techniques. In this book, we follow this data-centric view of ML systems and aim to provide a comprehensive overview of data management in ML systems for the end-to-end data science or ML lifecycle. We review multiple interconnected lines of work: (1) ML support in database (DB) systems, (2) DB-inspired ML systems, and (3) ML lifecycle systems. Covered topics include: in-database analytics via query generation and user-defined functions, factorized and statistical-relational learning; optimizing compilers for ML workloads; execution strategies and hardware accelerators; data access methods such as compression, partitioning and indexing; resource elasticity and cloud markets; as well as systems for data preparation for ML, model selection, model management, model debugging, and model serving. Given the rapidly evolving field, we strive for a balance between an up-to-date survey of ML systems, an overview of the underlying concepts and techniques, as well as pointers to open research questions. Hence, this book might serve as a starting point for both systems researchers and developers.
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