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Libros sobre el tema "Neural language models"

Autor: Grafiati

Publicado: 25 de mayo de 2024

Última modificación: 31 de julio de 2025

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1

1957-, Houghton George, ed. Connectionist models in cognitive psychology. Psychology Press, 2004.

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2

Miikkulainen, Risto. Subsymbolic natural language processing: An integrated model of scripts, lexicon, and memory. MIT Press, 1993.

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3

Bavaeva, Ol'ga. Metaphorical parallels of the neutral nomination "man" in modern English. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1858259.

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The monograph is devoted to a multidimensional analysis of metaphor in modern English as a parallel nomination that exists along with a neutral equivalent denoting a person. The problem of determining the essence of metaphorical names and their role in the language has attracted the attention of many foreign and domestic linguists on the material of various languages, but until now the fact of the parallel existence of metaphors and neutral nominations has not been emphasized. The research is in line with modern problems of linguistics related to the relationship of language, thinking an

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4

Arbib, Michael. Neural Models of Language Processes. Elsevier Science & Technology Books, 2012.

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5

Cairns, Paul, Joseph P. Levy, Dimitrios Bairaktaris, and John A. Bullinaria. Connectionist Models of Memory and Language. Taylor & Francis Group, 2015.

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6

Dimitoglou, George, and Ahmad Tafti. Artificial Intelligence: Machine Learning, Convolutional Neural Networks and Large Language Models. de Gruyter GmbH, Walter, 2024.

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7

Dimitoglou, George, and Ahmad Tafti. Artificial Intelligence: Machine Learning, Convolutional Neural Networks and Large Language Models. de Gruyter GmbH, Walter, 2024.

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8

Dimitoglou, George, and Ahmad Tafti. Artificial Intelligence: Machine Learning, Convolutional Neural Networks and Large Language Models. de Gruyter GmbH, Walter, 2024.

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9

Connectionist Models in Cognitive Psychology. Taylor & Francis Group, 2014.

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10

Houghton, George. Connectionist Models in Cognitive Psychology. Taylor & Francis Group, 2004.

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11

Houghton, George. Connectionist Models in Cognitive Psychology. Taylor & Francis Group, 2004.

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12

Houghton, George. Connectionist Models in Cognitive Psychology. Taylor & Francis Group, 2004.

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13

Houghton, George. Connectionist Models in Cognitive Psychology. Taylor & Francis Group, 2004.

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14

Houghton, George. Connectionist Models in Cognitive Psychology. Taylor & Francis Group, 2004.

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15

Computational Neuroscience: Trends in Research, 1997 (Language of Science). Springer, 1997.

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16

Neural Control of Speech. MIT Press, 2016.

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17

Neural Control of Speech. MIT Press, 2016.

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18

Gomez-Perez, Jose Manuel, Ronald Denaux, and Andres Garcia-Silva. Practical Guide to Hybrid Natural Language Processing: Combining Neural Models and Knowledge Graphs for NLP. Springer International Publishing AG, 2021.

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19

Deep Learning with Keras: Implementing Deep Learning Models and Neural Networks with the Power of Python. de Gruyter GmbH, Walter, 2017.

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20

Gomez-Perez, Jose Manuel, Ronald Denaux, and Andres Garcia-Silva. A Practical Guide to Hybrid Natural Language Processing: Combining Neural Models and Knowledge Graphs for NLP. Springer, 2020.

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21

Mishra, Pradeepta. PyTorch Recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models. Apress L. P., 2022.

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22

Kumar, Rahul, Matthew Lamons, and Abhishek Nagaraja. Python Deep Learning Projects: 9 projects demystifying neural network and deep learning models for building intelligent systems. Packt Publishing, 2018.

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23

Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles. Packt Publishing - ebooks Account, 2017.

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24

Bali, Raghav, Dipanjan Sarkar, and Tamoghna Ghosh. Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras. Packt Publishing, 2018.

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25

Reese, Richard M., and AshishSingh Bhatia. Natural Language Processing with Java: Techniques for building machine learning and neural network models for NLP, 2nd Edition. Packt Publishing - ebooks Account, 2018.

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26

Kalin, Josh. Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using Python, TensorFlow, and Keras. Packt Publishing, 2018.

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27

Deep Learning with PyTorch: A practical approach to building neural network models using PyTorch. Packt Publishing, 2018.

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28

Julian, David. Deep Learning with Pytorch Quick Start Guide: Learn to Train and Deploy Neural Network Models in Python. Packt Publishing, Limited, 2018.

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29

Hwang, Yoon Hyup. C# Machine Learning Projects: Nine real-world projects to build robust and high-performing machine learning models with C#. Packt Publishing, 2018.

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30

Whitenack, Daniel. Machine Learning With Go: Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language. Packt Publishing - ebooks Account, 2017.

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31

R Deep Learning Essentials: A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet, 2nd Edition. Packt Publishing, 2018.

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32

McNamara, Patrick, and Magda Giordano. Cognitive Neuroscience and Religious Language. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190636647.003.0005.

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Communication between deities and human beings rests on the use of language. Religious language has peculiarities such as the use of a formal voice, reductions in first-person and elevation of third-person pronoun use, archaistic elements, and an abundance of speech acts—features that reflect and facilitate the binding of the individual to conceived ultimate reality and value, decentering the Self while focusing on the deity. Explorations of the neurologic correlates of these cognitive and linguistic processes may be useful to identify constraints on neurocognitive models of religious language

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33

Strevens, Michael. The Whole Story. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199685509.003.0005.

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Causal explanations in the high-level sciences typically black-box the low-level details of the causal mechanisms that they invoke to account for their explananda: economists’ black-box psychological processes, psychologists’ black-box neural processes, and so on. Are these black-boxing explanatory models complete explanations of the phenomena in question, or are they just sketches of or templates for the whole explanatory story? This chapter poses a focused version of the question in the context of convergent evolution, the existence of which appears to show that underlying mechanisms are com

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34

Ratcliff, Roger, and Philip Smith. Modeling Simple Decisions and Applications Using a Diffusion Model. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.3.

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The diffusion model is one of the major sequential-sampling models for two-choice decision-making and choice response time in psychology. The model conceives of decision-making as a process in which noisy evidence is accumulated until one of two response criteria is reached and the associated response is made. The criteria represent the amount of evidence needed to make each decision and reflect the decision maker’s response biases and speed-accuracy trade-off settings. In this chapter we examine the application of the diffusion model in a variety of different settings. We discuss the optimali

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35

Papanicolaou, Andrew C., and Marina Kilintari. Imaging the Networks of Language. Edited by Andrew C. Papanicolaou. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199764228.013.15.

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Among the “higher” functions, language and its cerebral networks is the most intensively explored through behavioral or clinical studies and, more recently, through functional neuroimaging. From the former studies, several models (only partially congruent) have emerged during the past three centuries regarding the organization and topography of the brain mechanisms of the acoustic, phonological, semantic, syntactic, and pragmatic operations in which psycholinguists have divided the language function. The main task of this chapter is to extract from the vast functional neuroimaging literature o

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36

Zerilli, John. The Adaptable Mind. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190067885.001.0001.

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What conception of mental architecture can survive the evidence of neuroplasticity and neural reuse in the human brain? In particular, what sorts of modules are compatible with this evidence? This book shows how developmental and adult neuroplasticity, as well as evidence of pervasive neural reuse, force a revision to the standard conceptions of modularity and spell the end of a hardwired and dedicated language module. It argues from principles of both neural reuse and neural redundancy that language is facilitated by a composite of modules (or module-like entities), few if any of which are li

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37

Bergen, Benjamin, and Nancy Chang. Embodied Construction Grammar. Edited by Thomas Hoffmann and Graeme Trousdale. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780195396683.013.0010.

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This chapter focuses on Embodied Construction Grammar (ECG), another computational implementation of Construction Grammar. It points out that the driving question of this framework is how language is used in actual physical and social contexts, and explains that ECG is an attempt to computationally model the cognitive and neural mechanisms that underlie human linguistic behavior. The chapter evaluates the role of mental simulation in processing and outlines how language can be seen as in interface to simulation. It also shows how constructions are represented in ECG and describes an ECG-based

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38

Seneque, Gareth, and Darrell Chua. Hands-On Deep Learning with Go: A Practical Guide to Building and Implementing Neural Network Models Using Go. Packt Publishing, Limited, 2019.

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39

Wiley, Joshua F., Yuxi (Hayden) Liu, Pablo Maldonado, and Mark Hodnett. Deep Learning with R for Beginners: Design Neural Network Models in R 3. 5 Using TensorFlow, Keras, and MXNet. Packt Publishing, Limited, 2019.

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40

Sangeetha, V., and S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.

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Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip it

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41

Butz, Martin V., and Esther F. Kutter. How the Mind Comes into Being. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.001.0001.

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For more than 2000 years Greek philosophers have thought about the puzzling introspectively assessed dichotomy between our physical bodies and our seemingly non-physical minds. How is it that we can think highly abstract thoughts, seemingly fully detached from actual, physical reality? Despite the obvious interactions between mind and body (we get tired, we are hungry, we stay up late despite being tired, etc.), until today it remains puzzling how our mind controls our body, and vice versa, how our body shapes our mind. Despite a big movement towards embodied cognitive science over the last 20

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42

Bourhis, Richard Y., and Annie Montreuil. Acculturation, Vitality, and Bilingual Healthcare. Edited by Seth J. Schwartz and Jennifer Unger. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190215217.013.27.

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This chapter provides a conceptual framework for examining the delivery of bilingual healthcare for linguistic minorities in Canada’s Bilingual Belt. First, the chapter provides an overview of the ethnolinguistic vitality framework accounting for the sociostructural factors affecting the strength of minority and majority language communities within multilingual countries. Second, the interactive acculturation model (IAM) helps account for relations between high- and low-vitality group speakers whose intercultural relations may be harmonious, problematic, or conflictual. Third, the chapter prov

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43

Mariani, Giorgio. The Rhetorical Equivalent of War. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252039751.003.0003.

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This chapter examines how the rhetoric of war may be turned against war by focusing on the views of William James, Kenneth Burke, and Stephen Crane. Recent literary criticism has suggested that, far from being powerless or simply neutral vis-à-vis the armed conflicts it seeks to represent, language is complicit with violence. This understanding of the relationship between language and violence has been filed by James Dawes under the rubric of “the disciplinary model”—a model that conceives language and violence “as mutually constitutive.” This chapter first considers the ways in which the hard

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44

van der Hulst, Harry. Palatal harmony. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198813576.003.0004.

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This chapter applies the model that was developed in Chapters 2 and 3 to some well-known and well-studied cases of palatal vowel harmony. Palatal harmony is common in, and almost confined to, Finno-Ugric and Altaic languages. In the latter, palatal harmony often co-occurs with labial harmony. The chapter first discusses variation in the behavior of neutral vowels in Balto-Finnic languages and some special cases in this family. It then analyzes palatal harmony systems in Hungarian and considers the systems of several other languages. The focus is on asymmetries in vowel harmony involving dishar

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45

Zhai, Xiaoming, and Joseph Krajcik, eds. Uses of Artificial Intelligence in STEM Education. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/oso/9780198882077.001.0001.

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Abstract In the age of rapid technological advancements, the integration of artificial intelligence (AI), machine learning (ML), and large language models (LLMs) in science, technology, engineering, and mathematics (STEM) education has emerged as a transformative force, reshaping pedagogical approaches and assessment methodologies. This book, comprising twenty-six chapters, delves deep into the multifaceted realm of AI-driven STEM education. It begins by exploring the challenges and opportunities of AI-based STEM education, emphasizing the intricate balance between human tasks and technologica

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46

Nonlinear Workbook: Chaos, Fractals, Cellular Automata, Genetic Algorithms, Gene Expression Programming, Support Vector Machine, Wavelets, Hidden Markov Models, Fuzzy Logic with C++, Java and Symbolicc++ Programs 6th Edition. World Scientific Publishing Co Pte Ltd, 2014.

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47

Nonlinear Workbook: Chaos, Fractals, Cellular Automata, Neural Networks, Genetic Algorithms, Gene Expression Programming, Support Vector Machine, Wavelets, Hidden Markov Models, Fuzzy Logic with C++, Java and SymbolicC++ Programs. World Scientific Publishing Co Pte Ltd, 2005.

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48

Nolte, David D. Introduction to Modern Dynamics. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198844624.001.0001.

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Introduction to Modern Dynamics: Chaos, Networks, Space and Time (2nd Edition) combines the topics of modern dynamics—chaos theory, dynamics on complex networks and the geometry of dynamical spaces—into a coherent framework. This text is divided into four parts: Geometric Mechanics, Nonlinear Dynamics, Complex Systems, and Relativity. These topics share a common and simple mathematical language that helps students gain a unified physical intuition. Geometric mechanics lays the foundation and sets the tone for the rest of the book by emphasizing dynamical spaces, like state space and phase spac

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49

The nonlinear workbook: Chaos, fractals, cellular automata, neural networks, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, Fuzzy logic with C++, Java and SymbolicC++ programs. 4th ed. World Scientific, 2008.

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50

The nonlinear workbook: Chaos, fractals, cellular automata, neural networks, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, Fuzzy logic with C++, Java and SymbolicC++ programs. 3rd ed. World Scientific, 2005.

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