Machine Learning in Chemistry: The Impact of Artificial Intelligence, Cartwright Hugh M.
Автор: Atkins, Peter (Fellow of Lincoln College, University of Oxford) De Paula, Julio (Professor of Chemistry and Dean of College of Arts & Sciences, Lewis Название: Physical Chemistry for the Life Sciences, 2010 ISBN: 0199564280 ISBN-13(EAN): 9780199564286 Издательство: Oxford Academ Рейтинг: Цена: 156280.00 T Наличие на складе: Поставка под заказ. Описание: Provides a rich collection of analytical essays penned by those who have been closely associated with Pradeep worldwide. The broad message that comes from the contributions is the importance of open global trading systems, competitive and contestable markets domestically, coordination and regulation of national and global action on this, effective partnerships and representative global governance.
Автор: Buduma Nikhil Название: Fundamentals of Deep Learning: Designing Next-Generation Artificial Intelligence Algorithms ISBN: 1491925612 ISBN-13(EAN): 9781491925614 Издательство: Wiley Рейтинг: Цена: 36950.00 T Наличие на складе: Невозможна поставка. Описание: In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. If you`re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started.
Автор: Muggleton Stephen, Watanabe Hiroaki Название: Latest Advances in Inductive Logic Programming ISBN: 1783265086 ISBN-13(EAN): 9781783265084 Издательство: World Scientific Publishing Рейтинг: Цена: 85530.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book represents a selection of papers presented at the Inductive Logic Programming (ILP) workshop held at Cumberland Lodge, Great Windsor Park.
Автор: Fatima Название: Principles of Automated Negotiation ISBN: 1107002540 ISBN-13(EAN): 9781107002548 Издательство: Cambridge Academ Рейтинг: Цена: 50680.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: With an increasing number of applications in the context of multi-agent systems, automated negotiation is a rapidly growing area. Written by top researchers in the field, this state-of-the-art treatment of the subject explores key issues involved in the design of negotiating agents, covering strategic, heuristic, and axiomatic approaches. The authors discuss the potential benefits of automated negotiation as well as the unique challenges it poses for computer scientists and for researchers in artificial intelligence. They also consider possible applications and give readers a feel for the types of domains where automated negotiation is already being deployed. This book is ideal for graduate students and researchers in computer science who are interested in multi-agent systems. It will also appeal to negotiation researchers from disciplines such as management and business studies, psychology and economics.
Автор: Hurwitz, Kaufman Marcia, Bowles Adrian Название: Cognitive Computing: Implementing Big Data Machine Learning Solutions ISBN: 1118896629 ISBN-13(EAN): 9781118896624 Издательство: Wiley Рейтинг: Цена: 40120.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: A comprehensive guide to learning technologies that unlock the value in big data Cognitive Computing provides detailed guidance toward building a new class of systems that learn from experience and derive insights to unlock the value of big data.
Автор: Gerhard Wei? Название: Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments ISBN: 3540629343 ISBN-13(EAN): 9783540629344 Издательство: Springer Рейтинг: Цена: 65210.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This report documents current and ongoing developments in the area of learning in distributed artificial intelligence systems. The interdisciplinary co-operation of researchers from DAI and machine learning has established an active area of research and development.
Автор: Marcos Lopez de Prado Название: Advances in Financial Machine Learning ISBN: 1119482089 ISBN-13(EAN): 9781119482086 Издательство: Wiley Рейтинг: Цена: 44350.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Learn to understand and implement the latest machine learning innovations to improve your investment performance
Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that - until recently - only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest.
In the book, readers will learn how to:
Structure big data in a way that is amenable to ML algorithms
Conduct research with ML algorithms on big data
Use supercomputing methods and back test their discoveries while avoiding false positives
Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting.
Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.
Автор: Riguzzi Fabrizio Название: Foundations of Probabilistic Logic Programming: Languages, Semantics, Inference and Learning ISBN: 8770220182 ISBN-13(EAN): 9788770220187 Издательство: Taylor&Francis Рейтинг: Цена: 93910.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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.
Автор: Purnomo Hindriyanto Dwi Название: Computational Intelligence in the Internet of Things ISBN: 1522579559 ISBN-13(EAN): 9781522579557 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 188100.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In recent years, the need for smart equipment has increased exponentially with the upsurge in technological advances. To work to their fullest capacity, these devices need to be able to communicate with other devices in their network to exchange information and receive instructions. Computational Intelligence in the Internet of Things is an essential reference source that provides relevant theoretical frameworks and the latest empirical research findings in the area of computational intelligence and the Internet of Things. Featuring research on topics such as data analytics, machine learning, and neural networks, this book is ideally designed for IT specialists, managers, professionals, researchers, and academicians.
Автор: Boehm Matthias, Kumar Arun, Yang Jun Название: Data Management in Machine Learning Systems ISBN: 1681734966 ISBN-13(EAN): 9781681734965 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 67450.00 T Наличие на складе: Невозможна поставка. 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.
Автор: Dong Guozhu Название: Exploiting the Power of Group Differences: Using Patterns to Solve Data Analysis Problems ISBN: 1681735040 ISBN-13(EAN): 9781681735047 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 77610.00 T Наличие на складе: Невозможна поставка. Описание: This book presents pattern-based problem-solving methods for a variety of machine learning and data analysis problems. The methods are all based on techniques that exploit the power of group differences. They make use of group differences represented using emerging patterns (aka contrast patterns), which are patterns that match significantly different numbers of instances in different data groups. A large number of applications outside of the computing discipline are also included. Emerging patterns (EPs) are useful in many ways. EPs can be used as features, as simple classifiers, as subpopulation signatures/characterizations, and as triggering conditions for alerts. EPs can be used in gene ranking for complex diseases since they capture multi-factor interactions. The length of EPs can be used to detect anomalies, outliers, and novelties. Emerging/contrast pattern based methods for clustering analysis and outlier detection do not need distance metrics, avoiding pitfalls of the latter in exploratory analysis of high dimensional data. EP-based classifiers can achieve good accuracy even when the training datasets are tiny, making them useful for exploratory compound selection in drug design. EPs can serve as opportunities in opportunity-focused boosting and are useful for constructing powerful conditional ensembles. EP-based methods often produce interpretable models and results. In general, EPs are useful for classification, clustering, outlier detection, gene ranking for complex diseases, prediction model analysis and improvement, and so on. EPs are useful for many tasks because they represent group differences, which have extraordinary power. Moreover, EPs represent multi-factor interactions, whose effective handling is of vital importance and is a major challenge in many disciplines. Based on the results presented in this book, one can clearly say that patterns are useful, especially when they are linked to issues of interest. We believe that many effective ways to exploit group differences' power still remain to be discovered. Hopefully this book will inspire readers to discover such new ways, besides showing them existing ways, to solve various challenging problems.
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