Tensor Networks for Dimensionality Reduction and Large-Scale Optimization: Part 2 Applications and Future Perspectives 9781680832761

Варианты приобретения

Цена: 13656.00р.
Кол-во:
Наличие: Поставка под заказ. Есть в наличии на складе поставщика.
Склад Америка: Есть
При оформлении заказа до: 2025-07-23
Ориентировочная дата поставки: конец Сентября - начало Октября
При условии наличия книги у поставщика.

Добавить в корзину

в Мои желания

Автор: Cichocki Andrzej, Lee Namgil, Oseledets Ivan (Анджей Чихоцки)
Название: Tensor Networks for Dimensionality Reduction and Large-Scale Optimization: Part 2 Applications and Future Perspectives
Перевод названия: Анджей Чихоцки: Тензорные сети для уменьшения размерности и крупномасштабной оптимизации. Часть 2. П
ISBN: 9781680832761
Издательство: Now Publishers
Классификация:

Теория вычислительных машин и систем, кибернетика

Машинное обучение

ISBN-10: 168083276X
Обложка/Формат: Paperback
Страницы: 256
Вес: 0.37 кг.
Дата издания: 30.05.2017
Серия: Foundations and trends in machine learning
Язык: English
Размер: 234 x 156 x 14
Читательская аудитория: Tertiary education (us: college)
Подзаголовок: Part 2, applications and future perspectives
Рейтинг:
Поставляется из: США
Описание: This monograph builds on Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions by discussing tensor network models for super-compressed higher-order representation of data/parameters and cost functions, together with an outline of their applications in machine learning and data analytics. A particular emphasis is on elucidating, through graphical illustrations, that by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volume of data/parameters, thereby alleviating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification, generalized eigenvalue decomposition and in the optimization of deep neural networks. The monograph focuses on tensor train (TT) and Hierarchical Tucker (HT) decompositions and their extensions, and on demonstrating the ability of tensor networks to provide scalable solutions for a variety of otherwise intractable large-scale optimization problems. Tensor Networks for Dimensionality Reduction and Large-scale Optimization Parts 1 and 2 can be used as stand-alone texts, or together as a comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions. See also: Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions. ISBN 978-1-68083-222-8

Tensor Networks for Dimensionality Reduction and Large-Scale Optimization: Part 2 Applications and Future Perspectives, Cichocki Andrzej, Lee Namgil, Oseledets Ivan