Machine Learning for Asset Managers, Marcos Lopez de Prado
Автор: Strang Gilbert Название: Linear Algebra and Learning from Data ISBN: 0692196382 ISBN-13(EAN): 9780692196380 Издательство: Cambridge Academ Рейтинг: Цена: 66520.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
Автор: Christopher M. Bishop Название: Pattern Recognition and Machine Learning ISBN: 0387310738 ISBN-13(EAN): 9780387310732 Издательство: Springer Рейтинг: Цена: 79190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Автор: Kevin Murphy Название: Machine Learning ISBN: 0262018020 ISBN-13(EAN): 9780262018029 Издательство: MIT Press Рейтинг: Цена: 124150.00 T Наличие на складе: Невозможна поставка. Описание:
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.
The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package -- PMTK (probabilistic modeling toolkit) -- that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Автор: Campbell John Y. Название: Financial Decisions and Markets: A Course in Asset Pricing ISBN: 0691160805 ISBN-13(EAN): 9780691160801 Издательство: Wiley Рейтинг: Цена: 84480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
From the field's leading authority, the most authoritative and comprehensive advanced-level textbook on asset pricing
Financial Decisions and Markets is a graduate-level textbook that provides a broad overview of the field of asset pricing. John Campbell, one of the field's most respected authorities, introduces students to leading theories of portfolio choice, their implications for asset prices, and empirical patterns of risk and return in financial markets. Campbell emphasizes the interplay of theory and evidence, as theorists respond to empirical puzzles by developing models with new testable implications. Increasingly these models make predictions not only about asset prices but also about investors' financial positions, and they often draw on insights from behavioral economics.
After a careful introduction to single-period models, Campbell develops multiperiod models with time-varying discount rates, reviews the leading approaches to consumption-based asset pricing, and integrates the study of equities and fixed-income securities. He discusses models with heterogeneous agents who use financial markets to share their risks, but also may speculate against one another on the basis of different beliefs or private information. Campbell takes a broad view of the field, linking asset pricing to related areas, including financial econometrics, household finance, and macroeconomics. The textbook works in discrete time throughout, and does not require stochastic calculus. Problems are provided at the end of each chapter to challenge students to develop their understanding of the main issues in financial economics.
The most comprehensive and balanced textbook on asset pricing available, Financial Decisions and Marketswill be an essential resource for all graduate students in finance and related fields.
Integrated treatment of asset pricing theory and empirical evidence
Emphasis on investors' decisions
Broad view linking the field to areas including financial econometrics, household finance, and macroeconomics
Topics treated in discrete time, with no requirement for stochastic calculus
Solutions manual for problems available to professors
Автор: Darren Cook Название: Practical Machine Learning with H2O ISBN: 149196460X ISBN-13(EAN): 9781491964606 Издательство: Wiley Рейтинг: Цена: 42230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms.
Название: Learning machine translation ISBN: 0262072971 ISBN-13(EAN): 9780262072977 Издательство: MIT Press Рейтинг: Цена: 11390.00 T Наличие на складе: Нет в наличии. Описание: The Internet gives us access to a wealth of information in languages we don`t understand. The investigation of automated or semi-automated approaches to translation has become a thriving research field with enormous commercial potential. This title investigates how Machine Learning techniques can improve Statistical Machine Translation.
Автор: Seides Ted Название: So You Want to Start a Hedge Fund: Lessons for Managers and Allocators ISBN: 1119134188 ISBN-13(EAN): 9781119134183 Издательство: Wiley Рейтинг: Цена: 29570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Helpful, Accessible Guidance for Budding Hedge Funds So You Want to Start a Hedge Fund provides critical lessons and thoughtful insights to those trying to decipher the industry, as well as those seeking to invest in the next generation of high performers.
Автор: Conway Drew, White John Myles Название: Machine Learning for Hackers ISBN: 1449303714 ISBN-13(EAN): 9781449303716 Издательство: Wiley Рейтинг: Цена: 42230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Now that storage and collection technologies are cheaper and more precise, methods for extracting relevant information from large datasets is within the reach any experienced programmer willing to crunch data.
Автор: Koehn, Philipp Название: Statistical machine translation ISBN: 0521874157 ISBN-13(EAN): 9780521874151 Издательство: Cambridge Academ Рейтинг: Цена: 69690.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Automatic language translation systems like those used by Google, have been revolutionized by recent advances in the methods used in statistical machine translation. This first textbook on the topic explains these innovations carefully and shows the reader, whether a student or a developer, how to build their own translation system.
A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.
After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
Автор: Bradley Efron and Trevor Hastie Название: Computer Age Statistical Inference ISBN: 1107149894 ISBN-13(EAN): 9781107149892 Издательство: Cambridge Academ Рейтинг: Цена: 60190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
Автор: Raschka, Sebastian Mirjalili, Vahid Название: Python machine learning - ISBN: 1787125939 ISBN-13(EAN): 9781787125933 Издательство: Неизвестно Цена: 53940.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This second edition of Python Machine Learning by Sebastian Raschka is for developers and data scientists looking for a practical approach to machine learning and deep learning. In this updated edition, you`ll explore the machine learning process using Python and the latest open source technologies, including scikit-learn and TensorFlow 1.x.
Казахстан, 010000 г. Астана, проспект Туран 43/5, НП2 (офис 2) ТОО "Логобук" Тел:+7 707 857-29-98 ,+7(7172) 65-23-70 www.logobook.kz
