Автор: Brunton, Steven L. (university Of Washington) Kutz Название: Data-driven science and engineering ISBN: 1009098489 ISBN-13(EAN): 9781009098489 Издательство: Cambridge Academ Рейтинг: Цена: 7918.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data-driven discovery is revolutionizing how we model, predict, and control complex systems. This text integrates emerging machine learning and data science methods for engineering and science communities. Now with Python and MATLAB (R), new chapters on reinforcement learning and physics-informed machine learning, and supplementary videos and code.
Автор: Goodfellow Ian, Bengio Yoshua, Courville Aaron Название: Deep Learning ISBN: 0262035618 ISBN-13(EAN): 9780262035613 Издательство: MIT Press Рейтинг: Цена: 13543.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Автор: Kevin Murphy Название: Machine Learning ISBN: 0262018020 ISBN-13(EAN): 9780262018029 Издательство: MIT Press Рейтинг: Цена: 18622.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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: Mohri Mehryar, Rostamizadeh Afshin, Talwalkar Ameet Название: Foundations of Machine Learning, 2 ed. ISBN: 0262039400 ISBN-13(EAN): 9780262039406 Издательство: MIT Press Рейтинг: Цена: 12697.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.
This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.
Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVM); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes aoffer dditional material including concise probability review.
This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
Автор: Cuevas, Erik , Rodriguez, Alma Название: Metaheuristic Computation with MATLAB® ISBN: 0367438860 ISBN-13(EAN): 9780367438869 Издательство: Taylor&Francis Рейтинг: Цена: 18374.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The main purpose of this book is to provide a unified view of the most popular metaheuristic methods. Under this perspective, it has presented the fundamental design principles as well as the operators of metaheuristic approaches which are considered essential.
Описание: Consequently, EC researchers are enthusiastic about applying their arsenal for the design and optimization of deep neural networks (DNN). This book brings together the recent progress in DL research where the focus is particularly on three sub-domains that integrate EC with DL: (1) EC for hyper-parameter optimization in DNN;
Автор: Smys S., Bestak Robert, Rocha Бlvaro Название: Inventive Computation Technologies ISBN: 3030338487 ISBN-13(EAN): 9783030338480 Издательство: Springer Цена: 41925.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: With the intriguing development of technologies in several industries, along with the advent of ubiquitous computational resources, there are now ample opportunities to develop innovative computational technologies in order to solve a wide range of issues concerning uncertainty, imprecision, and vagueness in various real-life problems.
Автор: Dingle, Adair Название: Object-Oriented Design Choices ISBN: 0367820811 ISBN-13(EAN): 9780367820817 Издательство: Taylor&Francis Рейтинг: Цена: 21437.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book compares designs variant and emphasizes the strategic use of types in object-oriented design (OOD). In addition to thorough content coverage, many design problems are presented with sample solutions discussed in appendices. The book is partitioned into three sections that cover type design, coupling and reuse.
Автор: Miko?aj Boja?czyk; Alex Simpson Название: Foundations of Software Science and Computation Structures ISBN: 3030171264 ISBN-13(EAN): 9783030171261 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Universal Graphs and Good for Small Games Automata: New Tools for Infinite Duration Games.- Resource-Tracking Concurrent Games.- Change Actions: Models of Generalised Differentiation.- Coalgebra Learning via Duality.- Tight Worst-Case Bounds for Polynomial Loop Programs.- A Complete Normal-Form Bisimilarity for State.- Identifiers in Registers - Describing Network Algorithms with Logic.- The Impatient May Use Limited Optimism to Minimize Regret.- Causality in Linear Logic: Full Completeness and Injectivity (Unit-Free Multiplicative-Additive Fragment).- Rewriting Abstract Structures: Materialization Explained Categorically.- Two-Way Parikh Automata with a Visibly Pushdown Stack.- Kleene Algebra with Hypotheses.- Trees in Partial Higher Dimensional Automata.- The Bernays-Schoenfinkel-Ramsey Class of Separation Logic on Arbitrary Domains.- Continuous Reachability for Unordered Data Petri Nets is in PTime.- Optimal Satisfiability Checking for Arithmetic mu-Calculi.- Constructing Inductive-Inductive Types in Cubical Type Theory.- Causal Inference by String Diagram Surgery.- Higher-Order Distributions for Differential Linear Logic.- Languages Ordered by the Subword Order.- Strong Adequacy and Untyped Full-Abstraction for Probabilistic Coherence Spaces.- A Sound and Complete Logic for Algebraic Effects.- Equational Axiomatization of Algebras with Structure.- Towards a Structural Proof Theory of Probabilistic μ-Calculi.- Partial and Conditional Expectations in Markov Decision Processes with Integer Weights.- Equational Theories and Monads from Polynomial Cayley Representations.- A Dialectica-Like Interpretation of a Linear MSO on Infinite Words.- Deciding Equivalence of Separated Non-Nested Attribute Systems in Polynomial Time.- Justness: A Completeness Criterion for Capturing Liveness Properties.- Path category for Free - Open Morphisms from Coalgebras with Non-Deterministic Branching.
Автор: Eisenstein Jacob Название: Introduction to Natural Language Processing ISBN: 0262042843 ISBN-13(EAN): 9780262042840 Издательство: MIT Press Рейтинг: Цена: 12697.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learning techniques.
This textbook provides a technical perspective on natural language processing--methods for building computer software that understands, generates, and manipulates human language. It emphasizes contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning. The first section establishes a foundation in machine learning by building a set of tools that will be used throughout the book and applying them to word-based textual analysis. The second section introduces structured representations of language, including sequences, trees, and graphs. The third section explores different approaches to the representation and analysis of linguistic meaning, ranging from formal logic to neural word embeddings. The final section offers chapter-length treatments of three transformative applications of natural language processing: information extraction, machine translation, and text generation. End-of-chapter exercises include both paper-and-pencil analysis and software implementation.
The text synthesizes and distills a broad and diverse research literature, linking contemporary machine learning techniques with the field's linguistic and computational foundations. It is suitable for use in advanced undergraduate and graduate-level courses and as a reference for software engineers and data scientists. Readers should have a background in computer programming and college-level mathematics. After mastering the material presented, students will have the technical skill to build and analyze novel natural language processing systems and to understand the latest research in the field.
Автор: Moss Название: Logic, Language, Information, and Computation ISBN: 3662576686 ISBN-13(EAN): 9783662576687 Издательство: Springer Рейтинг: Цена: 9222.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Edited in collaboration with FoLLI, the Association of Logic, Language and Information this book constitutes the refereed proceedings of the 25th Workshop on Logic, Language, Information and Communication, WoLLIC 2018, held inBogota, Colombia, in July 2018.
Автор: Chrystopher L. Nehaniv Название: Computation for Metaphors, Analogy, and Agents ISBN: 3540659595 ISBN-13(EAN): 9783540659594 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Edited papers from an international workshop on Computation for Metaphors, Analogy, and Agents (CMAA `98). The papers cover research from a variety of disciplines from which have emerged aspects of descriptive, mathematical, computational or design knowledge concerning metaphor and analogy.
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