Stephan Raaijmakers's Books

Deep Learning for Natural Language Processing

By: Stephan Raaijmakers

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Deep Learning for Natural Language Processing

By: Stephan Raaijmakers

Explore the most challenging issues of natural language processing, and learn how to solve them with cutting-edge deep learning! Inside Deep Learning for Natural Language Processing you’ll find a wealth of NLP insights, including: An overview of NLP and deep learning One-hot text representations Word embeddings Models for textual similarity Sequential NLP Semantic role labeling Deep memory-based NLP Linguistic structure Hyperparameters for deep NLP Deep learning has advanced natural language processing to exciting new levels and powerful new applications! For the first time, computer systems can achieve "human" levels of summarizing, making connections, and other tasks that require comprehension and context. Deep Learning for Natural Language Processing reveals the groundbreaking techniques that make these innovations possible. Stephan Raaijmakers distills his extensive knowledge into useful best practices, real-world applications, and the inner workings of top NLP algorithms. About the technology Deep learning has transformed the field of natural language processing. Neural networks recognize not just words and phrases, but also patterns. Models infer meaning from context, and determine emotional tone. Powerful deep learning-based NLP models open up a goldmine of potential uses. About the book Deep Learning for Natural Language Processing teaches you how to create advanced NLP applications using Python and the Keras deep learning library. You’ll learn to use state-of the-art tools and techniques including BERT and XLNET, multitask learning, and deep memory-based NLP. Fascinating examples give you hands-on experience with a variety of real world NLP applications. Plus, the detailed code discussions show you exactly how to adapt each example to your own uses! What's inside Improve question answering with sequential NLP Boost performance with linguistic multitask learning Accurately interpret linguistic structure Master multiple word embedding techniques About the reader For readers with intermediate Python skills and a general knowledge of NLP. No experience with deep learning is required. About the author Stephan Raaijmakers is professor of Communicative AI at Leiden University and a senior scientist at The Netherlands Organization for Applied Scientific Research (TNO). Table of Contents PART 1 INTRODUCTION 1 Deep learning for NLP 2 Deep learning and language: The basics 3 Text embeddings PART 2 DEEP NLP 4 Textual similarity 5 Sequential NLP 6 Episodic memory for NLP PART 3 ADVANCED TOPICS 7 Attention 8 Multitask learning 9 Transformers 10 Applications of Transformers: Hands-on with BERT

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294

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Simon and Schuster

Published Date

ISBN 10

1638353999

ISBN 13

9781638353997

Computational Modeling in Cognition

Principles and Practice

By: Stephan Lewandowsky,Simon Farrell

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Computational Modeling in Cognition

Principles and Practice

By: Stephan Lewandowsky,Simon Farrell

An accessible introduction to the principles of computational and mathematical modeling in psychology and cognitive science This practical and readable work provides students and researchers, who are new to cognitive modeling, with the background and core knowledge they need to interpret published reports, and develop and apply models of their own. The book is structured to help readers understand the logic of individual component techniques and their relationships to each other.

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377

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SAGE

Published Date

ISBN 10

1452236194

ISBN 13

9781452236193

China’s Foreign-Invested Limited Partnership Enterprise

An Analysis of its Legal Personality, Limited Liability and Transferable Ownership Interest

By: Stephan Kuntner

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China’s Foreign-Invested Limited Partnership Enterprise

An Analysis of its Legal Personality, Limited Liability and Transferable Ownership Interest

By: Stephan Kuntner

Since a reform in 2010, foreign investors can establish a Foreign-Invested Limited Partnership Enterprise (FILPE) in China together with Chinese or foreign investors. The FILPE can be combined with a domestic or foreign corporate general partner, thus allowing for a structure that offers the flexibility and taxation conditions of a partnership while protecting its investors against personal liability like a company. The book explores from the perspective of a foreign investor if the FILPE is an attractive investment vehicle by analysing whether it provides the characteristics that are internationally recognized as constituting a standard corporate form. Among these characteristics, the three that are most strongly interconnected and interdependent form the core of the analysis: legal personality, limited liability and transferable ownership interest. These are analyzed in context of China's restrictive framework of foreign investment regulations and enterprise organization law.

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377

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Springer Nature

Published Date

ISBN 10

3030541819

ISBN 13

9783030541811

Deep Learning for Natural Language Processing

By: Stephan Raaijmakers

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Deep Learning for Natural Language Processing

By: Stephan Raaijmakers

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294

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Simon and Schuster

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ISBN 10

1638353999

ISBN 13

9781638353997

Bayesian Philosophy of Science

By: Jan Sprenger,Stephan Hartmann

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Bayesian Philosophy of Science

By: Jan Sprenger,Stephan Hartmann

How should we reason in science? Jan Sprenger and Stephan Hartmann offer a refreshing take on classical topics in philosophy of science, using a single key concept to explain and to elucidate manifold aspects of scientific reasoning. They present good arguments and good inferences as being characterized by their effect on our rational degrees of belief. Refuting the view that there is no place for subjective attitudes in 'objective science', Sprenger and Hartmann explain the value of convincing evidence in terms of a cycle of variations on the theme of representing rational degrees of belief by means of subjective probabilities (and changing them by Bayesian conditionalization). In doing so, they integrate Bayesian inference—the leading theory of rationality in social science—with the practice of 21st century science. Bayesian Philosophy of Science thereby shows how modeling such attitudes improves our understanding of causes, explanations, confirming evidence, and scientific models in general. It combines a scientifically minded and mathematically sophisticated approach with conceptual analysis and attention to methodological problems of modern science, especially in statistical inference, and is therefore a valuable resource for philosophers and scientific practitioners.

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Page Count

384

Publisher

Oxford University Press

Published Date

ISBN 10

0191652229

ISBN 13

9780191652226

Book's by Stephan Raaijmakers

Deep Learning for Natural Language Processing

Deep Learning for Natural Language Processing

Computational Modeling in Cognition

Computational Modeling in Cognition

China’s Foreign-Invested Limited Partnership Enterprise

China’s Foreign-Invested Limited Partnership Enterprise

Deep Learning for Natural Language Processing

Deep Learning for Natural Language Processing

Bayesian Philosophy of Science

Bayesian Philosophy of Science

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