Tabla de Contenidos: “…The dynamic unconscious: A linguistic action space organized by phonemic attractors -- Notes -- References -- Dissociation of body image and body schema and ways of embodiment -- Vectorial versus configural encoding of body space -- 1. …”

Tabla de Contenidos: “….) -- CAPÍTULO 30 METHODOLOGICAL AND LINGUISTIC TRAINING NEEDS OF PRE-SERVICE CLIL (...) -- CAPÍTULO 31 EL DEBATE UNIVERSITARIO COMO MÉTODO DE APRENDIZAJE DE ECONOMÍA EN (...) -- CAPÍTULO 32 MIRA, PREPARA Y ACTÚA EN EL MUNDO DEL TRABAJO MIGUEL ÁNGEL (...) -- CAPÍTULO 33 IMPLEMENTACIÓN DE ENTORNOS VIRTUALES DE APRENDIZAJE EN LA FORMACIÓN (...) -- CAPÍTULO 34 EDUCATIONAL MANAGEMENT FOR THE TRAINING OF WELLNESS MANAGERS FROM (...) -- CAPÍTULO 35 LOS INTERESES INDIVIDUALES DE APRENDIZAJE EN EL ALUMNADO DE TRABAJO (...) -- CAPÍTULO 36 LA SATISFACCIÓN Y LA INTERACCIÓN SOCIAL COMO FACTORES (...) -- CAPÍTULO 37 LAS IMPLICACIONES DE LA COVID-19 EN EL ALUMNADO UNIVERSITARIO (...) -- CAPÍTULO 38 IDEOLOGÍA, CULTURA DE MASAS Y EDUCACIÓN: ENSEÑAR CONCEPTOS (...) -- CAPÍTULO 39 EL USO DE SEMINARIOS VIRTUALES EN EL MARCO DE LOS ESTUDIOS DE (...) -- CAPÍTULO 40 RESULTADOS DE LA I JORNADA DE INNOVACIÓN DOCENTE DEL PROYECTO DE (...) -- CAPÍTULO 41 EL PAPEL DEL DOCENTE EN LA CONEXIÓN DE LA TEORÍA Y LA PRÁCTICA (...) -- CAPÍTULO 42 EL FILANDÓN COMO MÉTODO DE TRABAJO EN EL ESTUDIO DE LOS (...) -- CAPÍTULO 43 EL DERECHO DE LA SEGURIDAD SOCIAL A TRAVÉS DE LA PRENSA ESCRITA (...)…”

Tabla de Contenidos: “…MCC -- Human evaluation -- Benchmark tasks and datasets -- Defining the SuperGLUE benchmark tasks -- Running downstream tasks -- The Corpus of Linguistic Acceptability (CoLA) -- Stanford Sentiment TreeBank (SST-2) -- Microsoft Research Paraphrase Corpus (MRPC) -- Winograd schemas -- Summary -- Questions -- References -- Further reading -- Chapter 4: Advancements in Translations with Google Trax, Google Translate, and Gemini -- Defining machine translation -- Human transductions and translations -- Machine transductions and translations -- Evaluating machine translations -- Preprocessing a WMT dataset -- Preprocessing the raw data -- Finalizing the preprocessing of the datasets -- Evaluating machine translations with BLEU -- Geometric evaluations -- Applying a smoothing technique -- Translations with Google Trax -- Installing Trax -- Creating the Original Transformer model -- Initializing the model using pretrained weights -- Tokenizing a sentence -- Decoding from the Transformer -- De-tokenizing and displaying the translation -- Translation with Google Translate -- Translation with a Google Translate AJAX API Wrapper -- Implementing googletrans -- Translation with Gemini -- Gemini's potential -- Summary -- Questions -- References -- Further reading -- Chapter 5: Diving into Fine-Tuning through BERT -- The architecture of BERT -- The encoder stack -- Preparing the pretraining input environment -- Pretraining and fine-tuning a BERT model -- Fine-tuning BERT -- Defining a goal -- Hardware constraints -- Installing Hugging Face Transformers -- Importing the modules -- Specifying CUDA as the device for torch -- Loading the CoLA dataset -- Creating sentences, label lists, and adding BERT tokens -- Activating the BERT tokenizer -- Processing the data -- Creating attention masks -- Splitting the data into training and validation sets…”

Tabla de Contenidos: “…Multi-Sentence Reading Comprehension (MultiRC) -- Reading Comprehension with Commonsense Reasoning Dataset (ReCoRD) -- Recognizing Textual Entailment (RTE) -- Words in Context (WiC) -- The Winograd schema challenge (WSC) -- Running downstream tasks -- The Corpus of Linguistic Acceptability (CoLA) -- Stanford Sentiment TreeBank (SST-2) -- Microsoft Research Paraphrase Corpus (MRPC) -- Winograd schemas -- Summary -- Questions -- References -- Chapter 6: Machine Translation with the Transformer -- Defining machine translation -- Human transductions and translations -- Machine transductions and translations -- Preprocessing a WMT dataset -- Preprocessing the raw data -- Finalizing the preprocessing of the datasets -- Evaluating machine translation with BLEU -- Geometric evaluations -- Applying a smoothing technique -- Chencherry smoothing -- Translation with Google Translate -- Translations with Trax -- Installing Trax -- Creating the original Transformer model -- Initializing the model using pretrained weights -- Tokenizing a sentence -- Decoding from the Transformer -- De-tokenizing and displaying the translation -- Summary -- Questions -- References -- Chapter 7: The Rise of Suprahuman Transformers with GPT-3 Engines -- Suprahuman NLP with GPT-3 transformer models -- The architecture of OpenAI GPT transformer models -- The rise of billion-parameter transformer models -- The increasing size of transformer models -- Context size and maximum path length -- From fine-tuning to zero-shot models -- Stacking decoder layers -- GPT-3 engines -- Generic text completion with GPT-2 -- Step 9: Interacting with GPT-2 -- Training a custom GPT-2 language model -- Step 12: Interactive context and completion examples -- Running OpenAI GPT-3 tasks -- Running NLP tasks online -- Getting started with GPT-3 engines -- Running our first NLP task with GPT-3…”

Tabla de Contenidos: “…Probability -- 2.2.5 Features of Fuzzy Sets -- 2.3 Fuzzy Membership Functions -- 2.3.1 Some Popular Fuzzy Membership Functions -- 2.3.2 Transformations -- 2.3.3 Linguistic Variables -- 2.4 Operations on Fuzzy Sets -- 2.5 Fuzzy Relations -- 2.5.1 Crisp Relations -- 2.5.2 Fuzzy Relations -- 2.5.3 Operations on Fuzzy Relations -- 2.6 Fuzzy Extension Principle -- 2.6.1 Preliminaries -- 2.6.2 The Extension Principle -- Chapter Summary -- Solved Problems -- Test Your Knowledge -- Answers -- Exercises -- Bibliography and Historical Notes -- Chapter 3: Fuzzy Logic -- 3.1 Crisp Logic: A Review -- 3.1.1 Propositional Logic -- 3.1.2 Predicate Logic -- 3.1.3 Rules of Inference -- 3.2 Fuzzy Logic Basics -- 3.2.1 Fuzzy Truth Values -- 3.3 Fuzzy Truth in Terms of Fuzzy Sets -- 3.4 Fuzzy Rules -- 3.4.1 Fuzzy If-Then -- 3.4.2 Fuzzy If-Then-Else -- 3.5 Fuzzy Reasoning -- 3.5.1 Fuzzy Quantifiers -- 3.5.2 Generalized Modus Ponens -- 3.5.3 Generalized Modus Tollens -- Chapter Summary -- Solved Problems -- Test Your Knowledge -- Answers -- Exercises -- Bibliography and Historical Notes -- Chapter 4: Fuzzy Inference Systems -- Introduction -- 4.2 Fuzzification of the Input Variables -- 4.3 Application of Fuzzy Operators on the Antecedent Parts of the Rules…”

Tabla de Contenidos: “…Semantic Barrier -- 7.7. Linguistic Barriers -- 7.8. Past Experience -- 7.9. …”

“…This publication provides a diachronic overview of the main tendencies in Protestant theological jargon and academic language from the nineteenth to twenty-first centuries from a Germanist-linguistic perspective. Using analyses of special linguistic features in the works of representative authors and in predominant academic currents, it thoroughly illuminates the role of linguistic phenomena in Protestant theological jargon…”

“…In light of today's extensive use of digital communication, this volume focuses on how to understand and manage the various types of linguistically-based products that facilitate the use and extraction of information. …”
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“…In light of today's extensive use of digital communication, this volume focuses on how to understand and manage the various types of linguistically-based products that facilitate the use and extraction of information. …”
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“…In light of today's extensive use of digital communication, this volume focuses on how to understand and manage the various types of linguistically-based products that facilitate the use and extraction of information. …”
Enlace del recurso

“…This volume explores the diverse linguistic landscape of Southeast Asia's Chinese communities. …”

“…This book delves into the linguistic and cultural aspects of 'Kaaps,' a variety of Afrikaans primarily spoken in the Western Cape of South Africa. …”

“…Syntax of Dutch presents a synthesis of formal linguistic research of the Dutch language from over forty years of scholarship. …”

“…This volume provides a collection of chapters by a multidisciplinary collection of experts on the linguistic variegation of the later-Roman and post-imperial period in the Roman west. …”

Tabla de Contenidos: “…7.5 Applications of Cognitive Computing -- 7.5.1 Chatbots -- 7.5.2 Sentiment Analysis -- 7.5.3 Face Detection -- 7.5.4 Risk Assessment -- 7.6 Future of Cognitive Computing -- 7.7 Conclusion -- References -- 8 Tools Used for Research in Cognitive Engineering and Cyber Physical Systems -- 8.1 Cyber Physical Systems -- 8.2 Introduction: The Four Phases of Industrial Revolution -- 8.3 System -- 8.4 Autonomous Automobile System -- 8.4.1 The Timeline -- 8.5 Robotic System -- 8.6 Mechatronics -- References -- 9 Role of Recent Technologies in Cognitive Systems -- 9.1 Introduction -- 9.1.1 Definition and Scope of Cognitive Computing -- 9.1.2 Architecture of Cognitive Computing -- 9.1.3 Features and Limitations of Cognitive Systems -- 9.2 Natural Language Processing for Cognitive Systems -- 9.2.1 Role of NLP in Cognitive Systems -- 9.2.2 Linguistic Analysis -- 9.2.3 Example Applications Using NLP With Cognitive Systems -- 9.3 Taxonomies and Ontologies of Knowledge Representation for Cognitive Systems -- 9.3.1 Taxonomies and Ontologies and Their Importance in Knowledge Representation -- 9.3.2 How to Represent Knowledge in Cognitive Systems? …”