Recent Advances in Robot Learning, Judy A. Franklin; Tom M. Mitchell; Sebastian Thrun
Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman Название: The Elements of Statistical Learning ISBN: 0387848576 ISBN-13(EAN): 9780387848570 Издательство: Springer Рейтинг: Цена: 69870.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.
Автор: Goodfellow Ian, Bengio Yoshua, Courville Aaron Название: Deep Learning ISBN: 0262035618 ISBN-13(EAN): 9780262035613 Издательство: MIT Press Рейтинг: Цена: 90290.00 T 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: 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: Alberto Maria Segre Название: Machine Learning of Robot Assembly Plans ISBN: 0898382696 ISBN-13(EAN): 9780898382693 Издательство: Springer Рейтинг: Цена: 130430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This lack of unified purpose among the AI community makes this a very exciting time for AI research: new and diverse projects are springing up literally every day.
Автор: Buduma Nikhil Название: Fundamentals of Deep Learning: Designing Next-Generation Artificial Intelligence Algorithms ISBN: 1491925612 ISBN-13(EAN): 9781491925614 Издательство: Wiley Рейтинг: Цена: 36950.00 T Наличие на складе: Невозможна поставка. Описание: In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. If you`re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started.
Автор: Pier L. Lanzi; Wolfgang Stolzmann; Stewart W. Wils Название: Advances in Learning Classifier Systems ISBN: 3540424377 ISBN-13(EAN): 9783540424376 Издательство: Springer Рейтинг: Цена: 65210.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: These are the refereed post-proceedings of the Third International Workshop on Learning Classifier Systems, IWLCS 2000. The papers are organized in topical sections on theory, applications, and advanced architectures.
Автор: Ella Bingham Название: Advances in Independent Component Analysis and Learning Machines ISBN: 0128028068 ISBN-13(EAN): 9780128028063 Издательство: Elsevier Science Рейтинг: Цена: 128010.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining.
Examples of topics which have developed from the advances of ICA, which are covered in the book are:
A unifying probabilistic model for PCA and ICA
Optimization methods for matrix decompositions
Insights into the FastICA algorithm
Unsupervised deep learning
Machine vision and image retrieval
A review of developments in the theory and applications of independent component analysis, and its influence in important areas such as statistical signal processing, pattern recognition and deep learning
A diverse set of application fields, ranging from machine vision to science policy data
Contributions from leading researchers in the field
Автор: Barber Название: Bayesian Reasoning and Machine Learning ISBN: 0521518148 ISBN-13(EAN): 9780521518147 Издательство: Cambridge Academ Рейтинг: Цена: 73920.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available online and in the comprehensive software package include computer code, demos and teaching materials for instructors.
Автор: Mitchell Название: Machine Learning ISBN: 0071154671 ISBN-13(EAN): 9780071154673 Издательство: McGraw-Hill Рейтинг: Цена: 69770.00 T Наличие на складе: Поставка под заказ. Описание: Covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. This book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.
Автор: Ian H. Witten Название: Data Mining: Practical Machine Learning Tools and Techniques, ISBN: 0123748569 ISBN-13(EAN): 9780123748560 Издательство: Elsevier Science Рейтинг: Цена: 57970.00 T Наличие на складе: Поставка под заказ. Описание: Like the popular second edition, Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining?including both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. <br><br>Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download. <br><br>The book is a major revision of the second edition that appeared in 2005. While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years. The highlights for the updated new edition include completely revised technique sections; new chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new ?book release? version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on ?multi-instance learning?; new information on ranking the classification, plus comprehensive updates and modernization throughout. All in all, approximately 100 pages of new material.<br> <br><br>* Thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques<br><br>* Algorithmic methods at the heart of successful data mining?including tired and true methods as well as leading edge methods<br><br>* Performance improvement techniques that work by transforming the input or output<br><br>* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization?in an updated, interactive interface. <br>
Автор: Jeremy Wyatt; John Demiris Название: Advances in Robot Learning ISBN: 3540411623 ISBN-13(EAN): 9783540411628 Издательство: Springer Рейтинг: Цена: 65210.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Among the topics addressed in these papers are map building for robot navigation, multi-task reinforcement learning, neural network approaches, example- based learning, situated agents, planning maps for mobile robots, path finding, autonomous robots, and biologically inspired approaches.
Название: Recent advances in reinforcement learning ISBN: 0792397053 ISBN-13(EAN): 9780792397052 Издательство: Springer Рейтинг: Цена: 130430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Addresses research in the Artificial Intelligence and Neural Network communities. This book includes topics such as the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques.
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