Advances in Neural Computation, Machine Learning, and Cognitive Research VI, Kryzhanovsky

Автор: Brunton, Steven L. (university Of Washington) Kutz
Название: Data-driven science and engineering
ISBN: 1009098489 ISBN-13(EAN): 9781009098489
Издательство: Cambridge Academ
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Цена: 52790.00 T
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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
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Цена: 90290.00 T
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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
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Цена: 124150.00 T
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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: Michael R.W. Dawson
Название: Mind, Body, World: Foundations of Cognitive Science
ISBN: 1927356172 ISBN-13(EAN): 9781927356173
Издательство: Wiley EDC
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Цена: 38890.00 T
Наличие на складе: Невозможна поставка.
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Cognitive science arose in the 1950s when it became apparent that anumber of disciplines, including psychology, computer science,linguistics, and philosophy, were fragmenting. Perhaps owing to thefield’s immediate origins in cybernetics, as well as to thefoundational assumption that cognition is information processing,cognitive science initially seemed more unified than psychology.However, as a result of differing interpretations of the foundationalassumption and dramatically divergent views of the meaning of the terminformation processing, three separate schools emerged:classical cognitive science, connectionist cognitive science, andembodied cognitive science.

Examples, cases, and research findings taken from the wide range ofphenomena studied by cognitive scientists effectively explain andexplore the relationship among the three perspectives. Intended tointroduce both graduate and senior undergraduate students to thefoundations of cognitive science, Mind, Body, World addressesa number of questions currently being asked by those practicing in thefield: What are the core assumptions of the three different schools?What are the relationships between these different sets of coreassumptions? Is there only one cognitive science, or are there manydifferent cognitive sciences? Giving the schools equal treatment anddisplaying a broad and deep understanding of the field, Dawsonhighlights the fundamental tensions and lines of fragmentation thatexist among the schools and provides a refreshing and unifyingframework for students of cognitive science.

Автор: Azlan Iqbal; Matej Guid; Simon Colton; Jana Krivec
Название: The Digital Synaptic Neural Substrate
ISBN: 3319280783 ISBN-13(EAN): 9783319280783
Издательство: Springer
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Цена: 60940.00 T
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Описание: This new approach called the Digital Synaptic Neural Substrate (DSNS) mimics the brain`s ability to combine fragments of seemingly unrelated information from different domains (such as chess, photographs and music) to inspire itself to create new objects in any of them.

Автор: Dietmar Heinke; Glyn W. Humphreys; Andrew Olson
Название: Connectionist Models in Cognitive Neuroscience
ISBN: 185233052X ISBN-13(EAN): 9781852330521
Издательство: Springer
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Цена: 121110.00 T
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Описание: The theme of the 5th workshop in the series was Connectionist models in cognitive neuroscience`, and the workshop aimed to bring together papers focused on the inter-relations between functional (psychological) accounts of cognition and neural accounts of underlying brain processes, linked by connectionist models.

Автор: Tato, Maria Ines (Universidad de Buenos Aires, Argentina) Stanley, Peter Dalla Fontana, Luis Esteban Mclaughlin, Rob
Название: Swarm Intelligence and Evolutionary Computation
ISBN: 1032162503 ISBN-13(EAN): 9781032162508
Издательство: Taylor&Francis
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Цена: 132710.00 T
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Описание: This book aims at providing theoretical knowledge in the application of swarm intelligence and evolutionary computation including several recent meta-heuristic algorithms and also providing practical emerging applications in machine learning and deep learning.

Автор: Igor V. Tetko; V?ra K?rkov?; Pavel Karpov; Fabian
Название: Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation
ISBN: 3030304868 ISBN-13(EAN): 9783030304867
Издательство: Springer
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Цена: 98750.00 T
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Описание: The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019.

Автор: Goutam Kumar Bose, Pritam Pain
Название: Machine Learning Applications in Non-Conventional Machining Processes
ISBN: 1799836258 ISBN-13(EAN): 9781799836254
Издательство: Mare Nostrum (Eurospan)
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Цена: 159850.00 T
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Описание: Traditional machining has many limitations in today's technology-driven world, which has caused industrial professionals to begin implementing various optimization techniques within their machining processes. The application of methods including machine learning and genetic algorithms has recently transformed the manufacturing industry and created countless opportunities in non-traditional machining methods. Significant research in this area, however, is still considerably lacking.

Machine Learning Applications in Non-Conventional Machining Processes is a collection of innovative research on the advancement of intelligent technology in industrial environments and its applications within the manufacturing field. While highlighting topics including evolutionary algorithms, micro-machining, and artificial neural networks, this book is ideally designed for researchers, academicians, engineers, managers, developers, practitioners, industrialists, and students seeking current research on intelligence-based machining processes in today's technology-driven market.

Автор: Goutam Kumar Bose, Pritam Pain
Название: Machine Learning Applications in Non-Conventional Machining Processes
ISBN: 179983624X ISBN-13(EAN): 9781799836247
Издательство: Mare Nostrum (Eurospan)
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Цена: 206970.00 T
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Описание: Traditional machining has many limitations in today's technology-driven world, which has caused industrial professionals to begin implementing various optimization techniques within their machining processes. The application of methods including machine learning and genetic algorithms has recently transformed the manufacturing industry and created countless opportunities in non-traditional machining methods. Significant research in this area, however, is still considerably lacking.

Machine Learning Applications in Non-Conventional Machining Processes is a collection of innovative research on the advancement of intelligent technology in industrial environments and its applications within the manufacturing field. While highlighting topics including evolutionary algorithms, micro-machining, and artificial neural networks, this book is ideally designed for researchers, academicians, engineers, managers, developers, practitioners, industrialists, and students seeking current research on intelligence-based machining processes in today's technology-driven market.

Автор: Boris Kryzhanovsky; Witali Dunin-Barkowski; Vladim
Название: Advances in Neural Computation, Machine Learning, and Cognitive Research
ISBN: 3319666037 ISBN-13(EAN): 9783319666037
Издательство: Springer
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Цена: 149060.00 T
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Описание: This book describes new theories and applications of artificial neural networks, with a special focus on neural computation, cognitive science and machine learning.

Автор: Boris Kryzhanovsky; Witali Dunin-Barkowski; Vladim
Название: Advances in Neural Computation, Machine Learning, and Cognitive Research II
ISBN: 303013170X ISBN-13(EAN): 9783030131708
Издательство: Springer
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Цена: 214280.00 T
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Описание: This book describes new theories and applications of artificial neural networks, with a special focus on addressing problems in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large-scale neural models, brain–computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XX International Conference on Neuroinformatics, held in Moscow, Russia on October 8–12, 2018.