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: Sutton Richard S., Barto Andrew G. Название: Reinforcement Learning: An Introduction, 2 ed. ISBN: 0262039249 ISBN-13(EAN): 9780262039246 Издательство: MIT Press Рейтинг: Цена: 18850.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.
Like the first edition, this second edition focuses on core, online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new for the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Название: Introduction to Deep Learning ISBN: 3319730037 ISBN-13(EAN): 9783319730035 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website.Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism.This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.
Автор: Francois-Lavet Vincent, Henderson Peter, Islam Riashat Название: An Introduction to Deep Reinforcement Learning ISBN: 1680835386 ISBN-13(EAN): 9781680835380 Издательство: Неизвестно Рейтинг: Цена: 13656.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Provides a starting point for understanding deep reinforcement learning. Although written at a research level it provides a comprehensive and accessible introduction to deep reinforcement learning models, algorithms and techniques.
Автор: Boyd Stephen Название: Introduction to Applied Linear Algebra ISBN: 1316518965 ISBN-13(EAN): 9781316518960 Издательство: Cambridge Academ Рейтинг: Цена: 6811.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A groundbreaking introductory textbook covering the linear algebra methods needed for data science and engineering applications. It combines straightforward explanations with numerous practical examples and exercises from data science, machine learning and artificial intelligence, signal and image processing, navigation, control, and finance.
Описание: Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications.
Автор: Coley, David Название: Introduction to genetic algorithms for scientists and engineers ISBN: 9810236026 ISBN-13(EAN): 9789810236021 Издательство: World Scientific Publishing Рейтинг: Цена: 6494.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The approach taken in this text is largely practical, with algorithms being presented in full and working code (in BASIC, FORTRAN, PASCAL and C) on the accompanying disk. Exercises are included at the end of several chapters, many of which are computer based.
Автор: Masashi Sugiyama Название: Introduction to Statistical Machine Learning ISBN: 0128021217 ISBN-13(EAN): 9780128021217 Издательство: Elsevier Science Рейтинг: Цена: 17180.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials.
Introduction to Statistical Machine Learning provides ageneral introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks.
Provides the necessary background material to understand machine learning such as statistics, probability, linear algebra, and calculus
Complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning
Includes MATLAB/Octave programs so that readers can test the algorithms numerically and acquire both mathematical and practical skills in a wide range of data analysis tasks
Discusses a wide range of applications in machine learning and statistics and provides examples drawn from image processing, speech processing, natural language processing, robot control, as well as biology, medicine, astronomy, physics, and materials
Автор: Yang, Xin-She Название: Yang - INTRODUCTION TO ALGORITHMS FOR DATA MINING AND MAC... ISBN: 0128172169 ISBN-13(EAN): 9780128172162 Издательство: Elsevier Science Рейтинг: Цена: 9936.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning as well as optimization. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modelling skills so they can process and interpret data for classification, clustering, curve-fitting, and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.
Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics
Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study
Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages
Автор: Bishop Adrian N., del Moral Pierre, Niclas Angele Название: An Introduction to Wishart Matrix Moments ISBN: 1680835068 ISBN-13(EAN): 9781680835069 Издательство: Неизвестно Рейтинг: Цена: 12415.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Reviews and extends some important results in random matrix theory in the specific context of real random Wishart matrices. To overcome the complexity of the subject matter, the authors use a lecture note style to make the material accessible to a wide audience. This results in a comprehensive and self-contained introduction.
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