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: 1493938436 ISBN-13(EAN): 9781493938438 Издательство: Springer Рейтинг: Цена: 76160.00 T Наличие на складе: Ожидается поступление. 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: Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong Название: Mathematics for Machine Learning ISBN: 110845514X ISBN-13(EAN): 9781108455145 Издательство: Cambridge Academ Рейтинг: Цена: 42230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.
Автор: Kevin Murphy Название: Machine Learning ISBN: 0262018020 ISBN-13(EAN): 9780262018029 Издательство: MIT Press Рейтинг: Цена: 124150.00 T 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: Boyd Stephen Название: Introduction to Applied Linear Algebra ISBN: 1316518965 ISBN-13(EAN): 9781316518960 Издательство: Cambridge Academ Рейтинг: Цена: 45410.00 T 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.
Автор: Goulet James-A Название: Probabilistic Machine Learning for Civil Engineers ISBN: 0262538709 ISBN-13(EAN): 9780262538701 Издательство: MIT Press Рейтинг: Цена: 56430.00 T Наличие на складе: Невозможна поставка. Описание: An introduction to key concepts and techniques in probabilistic machine learning for civil engineering students and professionals; with many step-by-step examples, illustrations, and exercises.
This book introduces probabilistic machine learning concepts to civil engineering students and professionals, presenting key approaches and techniques in a way that is accessible to readers without a specialized background in statistics or computer science. It presents different methods clearly and directly, through step-by-step examples, illustrations, and exercises. Having mastered the material, readers will be able to understand the more advanced machine learning literature from which this book draws.
The book presents key approaches in the three subfields of probabilistic machine learning: supervised learning, unsupervised learning, and reinforcement learning. It first covers the background knowledge required to understand machine learning, including linear algebra and probability theory. It goes on to present Bayesian estimation, which is behind the formulation of both supervised and unsupervised learning methods, and Markov chain Monte Carlo methods, which enable Bayesian estimation in certain complex cases. The book then covers approaches associated with supervised learning, including regression methods and classification methods, and notions associated with unsupervised learning, including clustering, dimensionality reduction, Bayesian networks, state-space models, and model calibration. Finally, the book introduces fundamental concepts of rational decisions in uncertain contexts and rational decision-making in uncertain and sequential contexts. Building on this, the book describes the basics of reinforcement learning, whereby a virtual agent learns how to make optimal decisions through trial and error while interacting with its environment.
Автор: Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong Название: Mathematics for Machine Learning ISBN: 1108470041 ISBN-13(EAN): 9781108470049 Издательство: Cambridge Academ Рейтинг: Цена: 88710.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.
Автор: McClarren Ryan G. Название: Machine Learning for Engineers: Using Data to Solve Problems for Physical Systems ISBN: 3030703878 ISBN-13(EAN): 9783030703875 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: All engineers and applied scientists will need to harness the power of machine learning to solve the highly complex and data intensive problems now emerging.
Автор: Osvaldo Simeone Название: An Introduction to Quantum Machine Learning for Engineers ISBN: 1638280584 ISBN-13(EAN): 9781638280583 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 98870.00 T Наличие на складе: Нет в наличии. Описание: This monograph is motivated by a number of recent developments that appear to define a possible new role for researchers with an engineering profile. Software that make programming quantum algorithms more accessible. A new framework is emerging for programming quantum algorithms to be run on current quantum hardware.
Автор: Wright, Stephen J. (university Of Wisconsin, Madison) Recht, Benjamin (university Of California, Berkeley) Название: Optimization for data analysis ISBN: 1316518981 ISBN-13(EAN): 9781316518984 Издательство: Cambridge Academ Рейтинг: Цена: 40120.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Optimization techniques are at the core of data science. An understanding of the basic techniques and their fundamental properties provides important grounding for students, researchers, and practitioners. This compact, self-contained text covers the fundamentals of optimization algorithms, focusing on the techniques most relevant to data science.
Автор: Steven Smith Название: Digital Signal Processing: A Practical Guide for Engineers and Sc ISBN: 075067444X ISBN-13(EAN): 9780750674447 Издательство: Elsevier Science Рейтинг: Цена: 87570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In addition to its thorough coverage of DSP design and programming techniques, Smith also covers the operation and usage of DSP chips. He uses Analog Devices' popular DSP chip family as design examples. Also included on the companion website is technical info on DSP processors from the four major manufacturers (Analog Devices, Texas Instruments, Motorola, and Lucent) and other DSP software. *Covers all major DSP topics *Full of insider information and shortcuts *Basic techniques and algorithms explained without complex numbers
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.
The updated edition of this practical book uses concrete examples, minimal theory, and three production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.
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