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Semantics and Deep Learning
This Element covers the interaction of two research areas: linguistic semantics and deep learning. It focuses on three phenomena central to natural language interpretation: reasoning and inference; compositionality; extralinguistic grounding. Representation of these phenomena in recent neural models is discussed, along with the quality of these representations and ways to evaluate them (datasets, tests, measures). The Element closes with suggestions on possible deeper interactions between theoretical semantics and language technology based on deep learning models.
R 2,806.10
Semantics and Deep Learning
This Element covers the interaction of two research areas: linguistic semantics and deep learning. It focuses on three phenomena central to natural language interpretation: reasoning and inference; compositionality; extralinguistic grounding. Representation of these phenomena in recent neural models is discussed, along with the quality of these representations and ways to evaluate them (datasets, tests, measures). The Element closes with suggestions on possible deeper interactions between theoretical semantics and language technology based on deep learning models.
R 930.20
Deep Learning Enabled Semantic Communications
Comprehensive overview of the principles, theories, and techniques behind deep learning-enabled semantic communications Deep Learning Enabled Semantic Communications explores the synergy between deep learning and semantic communication, particularly in the context of advancing 6G networks. It provides a focused introduction to the subject, systematically covering deep learning-enabled semantic communication systems and task-oriented semantic transmission paradigms in wireless communication. The book reviews various aspects of semantic communications, including information theory, multimodal technologies, semantic noise, and semantic sensing. It explores cutting-edge semantic communication architectures, highlighting their advantages over traditional approaches and their potential to drive the future of intelligent information industry. The book also details applications of deep learning-based semantic communication systems across various sources, including text, speech, images, and videos, comprehensively addressing system design, performance optimization, and measurement metrics. The book is divided into eight main parts, which cover foundational knowledge, system design, multimodal and multitask-oriented semantic communication systems, joint semantic sensing and sampling, semantic noise suppression, and generative AI enabled systems. Written by a diverse group of experts in academia and research institutions, Deep Learning Enabled Semantic Communications includes information on: Fundamental knowledge about the deep learning and semantic communications, including the history, neural networks, and semantic information theoryCompression of multimodal inputs, extraction of global semantic information, and the design of neural networks to boost the capability of handling lengthy speechIncorporation of different sources to extract semantic features and serve diverse intelligent tasks at the receiverIntroduction of semantic impairments in communications to uncover how to design robust systemsJoint design of data sampling, compression, and coding schemes under the guidance of semantic informationFramework of generative semantic communications to detail the principles of incorporating generative models into semantic communications Deep Learning Enabled Semantic Communications is an essential learning resource and reference for graduate and undergraduate students pursuing degrees in wireless communications, signal processing, or deep learning as well as engineers in the telecommunications and IT industries focusing on wireless communication techniques.
R 4,559.00
Sentimental Analysis and Deep Learning
This book gathers selected papers presented at the International Conference on Sentimental Analysis and Deep Learning (ICSADL 2021), jointly organized by Tribhuvan University, Nepal; Prince of Songkla University, Thailand; and Ejesra during June, 18–19, 2021. The volume discusses state-of-the-art research works on incorporating artificial intelligence models like deep learning techniques for intelligent sentiment analysis applications. Emotions and sentiments are emerging as the most important human factors to understand the prominent user-generated semantics and perceptions from the humongous volume of user-generated data. In this scenario, sentiment analysis emerges as a significant breakthrough technology, which can automatically analyze the human emotions in the data-driven applications. Sentiment analysis gains the ability to sense the existing voluminous unstructured data and delivers a real-time analysis to efficiently automate the business processes. Meanwhile, deep learning emerges as the revolutionary paradigm with its extensive data-driven representation learning architectures. This book discusses all theoretical aspects of sentimental analysis, deep learning and related topics.
R 10,403.36
Vector Semantics
This open access book introduces Vector semantics, which links the formal theory of word vectors to the cognitive theory of linguistics. The computational linguists and deep learning researchers who developed word vectors have relied primarily on the ever-increasing availability of large corpora and of computers with highly parallel GPU and TPU compute engines, and their focus is with endowing computers with natural language capabilities for practical applications such as machine translation or question answering. Cognitive linguists investigate natural language from the perspective of human cognition, the relation between language and thought, and questions about conceptual universals, relying primarily on in-depth investigation of language in use. In spite of the fact that these two schools both have ‘linguistics’ in their name, so far there has been very limited communication between them, as their historical origins, data collection methods, and conceptual apparatuses are quite different. Vector semantics bridges the gap by presenting a formal theory, cast in terms of linear polytopes, that generalizes both word vectors and conceptual structures, by treating each dictionary definition as an equation, and the entire lexicon as a set of equations mutually constraining all meanings.
R 1,832.00
Cognitive Semantics of Artificial Intelligence: A New Perspective
This book addresses the issue of cognitive semantics’ aspects that cannot be represented by traditional digital and logical means. The problem of creating cognitive semantics can be resolved in an indirect way. The electromagnetic waves, quantum fields, beam of light, chaos control, relativistic theory, cosmic string recognition, category theory, group theory, and so on can be used for this aim. Since the term artificial intelligence (AI) appeared, various versions of logic have been created; many heuristics for neural networks deep learning have been made; new nature-like algorithms have been suggested. At the same time, the initial digital, logical, and neural network principles of representation of knowledge in AI systems have not changed a lot. The researches of these aspects of cognitive semantics of AI are based on the author's convergent methodology, which provides the necessary conditions for purposeful and sustainable convergence of decision-making.
R 3,103.31
Advanced Introduction to Semantics
This book is an advanced introduction to semantics that presents this crucial component of human language through the lens of the 'Meaning-Text' theory - an approach that treats linguistic knowledge as a huge inventory of correspondences between thought and speech. Formally, semantics is viewed as an organized set of rules that connect a representation of meaning (Semantic Representation) to a representation of the sentence (Deep-Syntactic Representation). The approach is particularly interesting for computer assisted language learning, natural language processing and computational lexicography, as our linguistic rules easily lend themselves to formalization and computer applications. The model combines abstract theoretical constructions with numerous linguistic descriptions, as well as multiple practice exercises that provide a solid hands-on approach to learning how to describe natural language semantics.
R 5,701.95
Advanced Introduction to Semantics
This book is an advanced introduction to semantics that presents this crucial component of human language through the lens of the 'Meaning-Text' theory - an approach that treats linguistic knowledge as a huge inventory of correspondences between thought and speech. Formally, semantics is viewed as an organized set of rules that connect a representation of meaning (Semantic Representation) to a representation of the sentence (Deep-Syntactic Representation). The approach is particularly interesting for computer assisted language learning, natural language processing and computational lexicography, as our linguistic rules easily lend themselves to formalization and computer applications. The model combines abstract theoretical constructions with numerous linguistic descriptions, as well as multiple practice exercises that provide a solid hands-on approach to learning how to describe natural language semantics.
R 2,003.13
Spreading Activation, Lexical Priming and the Semantic Web
This book explores the interconnections between linguistics and Artificial Intelligence (AI) research, their mutually influential theories and developments, and the areas where these two groups can still learn from each other. It begins with a brief history of artificial intelligence theories focusing on figures including Alan Turing and M. Ross Quillian and the key concepts of priming, spread-activation and the semantic web. The author details the origins of the theory of lexical priming in early AI research and how it can be used to explain structures of language that corpus linguists have uncovered. He explores how the idea of mirroring the mind’s language processing has been adopted to create machines that can be taught to listen and understand human speech in a way that goes beyond a fixed set of commands. In doing so, he reveals how the latest research into the semantic web and Natural Language Processing has developed from its early roots. The book moves on to describe how the technology has evolved with the adoption of inference concepts, probabilistic grammar models, and deep neural networks in order to fine-tune the latest language-processing and translation tools. This engaging book offers thought-provoking insights to corpus linguists, computational linguists and those working in AI and NLP.
R 2,148.60
Machine Learning and Deep Learning Techniques for Medical Image Recognition
Machine Learning and Deep Learning Techniques for Medical Image Recognition comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks with the theory, and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples.Features:Offers important key aspects in the development and implementation of ML and DL approaches toward developing prediction tools and models and improving medical diagnosis Teaches how ML and DL algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy, and pathology and so forth Covers common research problems in medical image analysis and their challenges Focusses on aspects of deep learning and machine learning for combating COVID-19 Includes pertinent case studies This book is aimed at researchers and graduate students in computer engineering, artificial intelligence and machine learning, and biomedical imaging.
R 7,576.15
Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics
This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory. The book’s chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval.The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.
R 6,232.26
Knowledge Graphs: Semantics, Machine Learning, and Languages
Semantic computing is an integral part of modern technology, an essential component of fields as diverse as artificial intelligence, data science, knowledge discovery and management, big data analytics, e-commerce, enterprise search, technical documentation, document management, business intelligence, and enterprise vocabulary management.This book presents the proceedings of SEMANTICS 2023, the 19th International Conference on Semantic Systems, held in Leipzig, Germany, from 20 to 22 September 2023. The conference is a pivotal event for those professionals and researchers actively engaged in harnessing the power of semantic computing, an opportunity to increase their understanding of the subject’s transformative potential while confronting its practical limitations. Attendees include information managers, IT architects, software engineers, and researchers from a broad spectrum of organizations, including research facilities, non-profit entities, public administrations, and the world′s largest corporations. For this year’s conference a total of 54 submissions were received in response to a call for papers. These were subjected to a rigorous, double-blind review process, with at least three independent reviews conducted for each submission. The 16 papers included here were ultimately accepted for presentation, with an acceptance rate of 29.6%. Areas covered include novel research challenges in areas such as data science, machine learning, logic programming, content engineering, social computing, and the Semantic Web. The book provides an up-to-date overview, which will be of interest to all those wishing to stay abreast of emerging trends and themes within the vast field of semantic computing.
R 7,107.00
Handbook of Deep Learning Applications
This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.
R 8,552.21
Advanced Deep Learning with TensorFlow 2 and Keras
Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and KerasKey FeaturesExplore the most advanced deep learning techniques that drive modern AI resultsNew coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentationCompletely updated for TensorFlow 2.xBook DescriptionAdvanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects.Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques.Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance.Next, you’ll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.What you will learnUse mutual information maximization techniques to perform unsupervised learningUse segmentation to identify the pixel-wise class of each object in an imageIdentify both the bounding box and class of objects in an image using object detectionLearn the building blocks for advanced techniques - MLPss, CNN, and RNNsUnderstand deep neural networks - including ResNet and DenseNetUnderstand and build autoregressive models – autoencoders, VAEs, and GANsDiscover and implement deep reinforcement learning methodsWho this book is forThis is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be helpful. Knowledge of Keras or TensorFlow 2.0 is not required but is recommended.
R 1,498.00
Deep Semantics and the Evolution of New Scientific Theories and Discoveries
Combining a variety of sounds to form words that can be understood by other individuals, language is one of the defining characteristics of the human species. However, since even highly educated people, great writers, and poets are not consistent regarding the meanings of words, we are unlikely to find consistent rules regarding word meanings by examining human language use. Therefore, deep semantics aims to study of the meanings of individual sounds and their role in creating the meanings of words. Deep Semantics and the Evolution of New Scientific Theories and Discoveries provides innovative insights into the mental processing of word meanings and lack of consistency in human use, while providing examples from different language sources such as, the Quran and Arabic text. This publication presents word roots, the human cognitive system, sound function, and knowledge process, and is designed for linguists, educators, speech professionals, researchers, students, and academics whose interests include topics on the study of people's imperfect views, feelings, and habits in using words.
R 5,438.53
Representation Learning
This monograph addresses advances in representation learning, a cutting-edge research area of machine learning. Representation learning refers to modern data transformation techniques that convert data of different modalities and complexity, including texts, graphs, and relations, into compact tabular representations, which effectively capture their semantic properties and relations. The monograph focuses on (i) propositionalization approaches, established in relational learning and inductive logic programming, and (ii) embedding approaches, which have gained popularity with recent advances in deep learning. The authors establish a unifying perspective on representation learning techniques developed in these various areas of modern data science, enabling the reader to understand the common underlying principles and to gain insight using selected examples and sample Python code. The monograph should be of interest to a wide audience, ranging from data scientists, machine learning researchers and students to developers, software engineers and industrial researchers interested in hands-on AI solutions.
R 7,129.08
Semantics
The subject of semantics has been appropriated by various disciplines including linguistic philosophy, logic, cognitive psychology, anthropological linguistics, and computer technology. As a result, it is difficult to define the study of semantics as an actual discipline without discovering what each field using a semantic approach to its subject matter has contributed to the understanding of what words mean. This volume is a result of those discoveries. Primarily an introductory work, this volume outlines the approaches that various disciplines have taken to the subject, attempts to show their relationships and their limitations, and presents the more important aspects of each approach -- from psychosemantics to artificial intelligence -- using pertinent source material from psychology, philosophy, logic, linguistics, and sociology. For individuals coming to the study of semantics for the first time, or those who are interested in what the overall study may offer beyond their specialization, this volume will provide a helpful overview of the subject.
R 2,380.60
Deep Learning for Engineers
Deep Learning for Engineers introduces fundamental principles of deep learning along with the explanation of basic elements required for understanding and applying deep learning models.As a comprehensive guideline for applying deep learning models in practical settings, this book features an easy-to-understand coding structure using Python and PyTorch with an in-depth explanation of four typical deep learning case studies on image classification, object detection, semantic segmentation, and image captioning. The fundamentals of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architectures and their practical implementations in science and engineering are also discussed.This book includes exercise problems for all case studies focusing on various fine-tuning approaches in deep learning. Science and engineering students at both undergraduate and graduate levels, academic researchers, and industry professionals will find the contents useful.
R 5,984.07
Deep Learning in Computer Vision
Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, for postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition.
R 5,651.25
Python Deep Learning Projects
Insightful projects to master deep learning and neural network architectures using Python and KerasKey FeaturesExplore deep learning across computer vision, natural language processing (NLP), and image processingDiscover best practices for the training of deep neural networks and their deploymentAccess popular deep learning models as well as widely used neural network architecturesBook DescriptionDeep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier.Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You’ll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system.Similarly, you’ll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you’ll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects.By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient wayWhat you will learnSet up a deep learning development environment on Amazon Web Services (AWS)Apply GPU-powered instances as well as the deep learning AMIImplement seq-to-seq networks for modeling natural language processing (NLP)Develop an end-to-end speech recognition systemBuild a system for pixel-wise semantic labeling of an imageCreate a system that generates images and their regionsWho this book is forPython Deep Learning Projects is for you if you want to get insights into deep learning, data science, and artificial intelligence. This book is also for those who want to break into deep learning and develop their own AI projects.It is assumed that you have sound knowledge of Python programming
R 1,661.00
Deep Learning for Engineers
Deep Learning for Engineers introduces fundamental principles of deep learning along with the explanation of basic elements required for understanding and applying deep learning models.As a comprehensive guideline for applying deep learning models in practical settings, this book features an easy-to-understand coding structure using Python and PyTorch with an in-depth explanation of four typical deep learning case studies on image classification, object detection, semantic segmentation, and image captioning. The fundamentals of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architectures and their practical implementations in science and engineering are also discussed.This book includes exercise problems for all case studies focusing on various fine-tuning approaches in deep learning. Science and engineering students at both undergraduate and graduate levels, academic researchers, and industry professionals will find the contents useful.
R 2,711.00
Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics
Data science revolves around two giants: Big Data analytics and Deep Learning. It is becoming challenging to handle and retrieve useful information due to how fast data is expanding. This book presents the technologies and tools to simplify and streamline the formation of Big Data as well as Deep Learning systems. This book discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and decision-making. It also covers numerous applications in healthcare, education, communication, media, and entertainment. Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics offers innovative platforms for integrating Big Data and Deep Learning and presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval. FEATURES Provides insight into the skill set that leverages one’s strength to act as a good data analyst Discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and help in decision-making Covers numerous potential applications in healthcare, education, communication, media, and entertainment Offers innovative platforms for integrating Big Data and Deep Learning Presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval from Big DataThis book is aimed at industry professionals, academics, research scholars, system modelers, and simulation experts.
R 2,945.80
Semantics
The study of meaning in language has developed dramatically over the last fifty years. Semantics is distinctive as it not only presents a general introduction to the topic, including the most recent developments, but it also provides a unique perspective for addressing current issues. It opens by introducing readers to the study of logic (natural deduction) as the background against which developments have taken place. This demonstrates the link between semantics and the study of reasoning and how this view can provide new solutions to the puzzles that have plagued the approaches presented in other textbooks. The major subject areas of semantics are discussed, including quantification, anaphora and discourse, tense and aspect, ellipsis and context, and word meaning. The book also presents state-of-the-art research in topics at the forefront of semantics.
R 3,021.43
Semantics
The study of meaning in language has developed dramatically over the last fifty years. Semantics is distinctive as it not only presents a general introduction to the topic, including the most recent developments, but it also provides a unique perspective for addressing current issues. It opens by introducing readers to the study of logic (natural deduction) as the background against which developments have taken place. This demonstrates the link between semantics and the study of reasoning and how this view can provide new solutions to the puzzles that have plagued the approaches presented in other textbooks. The major subject areas of semantics are discussed, including quantification, anaphora and discourse, tense and aspect, ellipsis and context, and word meaning. The book also presents state-of-the-art research in topics at the forefront of semantics.
R 5,569.34
Semantics
The latest edition of the bestselling introduction to the field of linguistic semantics, updated throughout and featuring a wholly new chapter on inferential pragmatics Semantics, Fifth Edition, is a comprehensive and well-balanced introduction to the study of the communication of meaning in language. Assuming no previous background in semantics and limited familiarity with formal linguistics, this student-friendly textbook describes the concepts, theory, and study of semantics in an accessible and clear style. Concise chapters describe the role of semantics within contemporary linguistics, cover key topics in the analysis of word and sentence meaning, and review major semantic theories such as componential theory, formal semantics, and cognitive semantics. The updated fifth edition incorporates recent theoretical developments and important research in linguistic semantics, featuring an entirely new chapter examining the overlap between inferential pragmatics and Relevance Theory, truth-conditional meaning, and other traditional areas of semantics. Revised and expanded sections discuss the continuing growth and consolidation of cognitive semantics, various contextual features of language, conceptualization and categorization, and construal and perspective. This edition includes new exercises with solutions, up-to-date references to relevant literature, and additional examples with data from a wide range of different languages. Covers basic concepts and methods as well as key theoretical models, current lines of research, and important writersExplains general concepts in semantics before gradually moving to more advanced topics in semantic description and theoretical approachesHighlights the relation between cross-linguistic variation and language universalsProvides students with the background necessary to understand more advanced and specialized primary semantics literatureIncludes a glossary of technical terms and numerous exercises arranged by level of difficulty Highlights the relationship between semantics and cross-linguistic variation, language universals, and pragmaticsWith detailed examples from a wide range of contexts and a wealth of practical exercises, Semantics, Fifth Edition, remains the perfect textbook for undergraduate students of linguistics, English language, applied linguistics, modern languages, and computer sciences.
R 1,617.00
Understanding Semantics
Understanding Semantics, Second Edition, provides an engaging and accessible introduction to linguistic semantics. The first part takes the reader through a step-by-step guide to the main phenomena and notions of semantics, covering levels and dimensions of meaning, ambiguity, meaning and context, logical relations and meaning relations, the basics of noun semantics, verb semantics and sentence semantics. The second part provides a critical introduction to the basic notions of the three major theoretical approaches to meaning: structuralism, cognitive semantics and formal semantics.Key features include:A consistent mentalist perspective on meaningBroad coverage of lexical and sentence semantics, including three new chapters discussing deixis, NP semantics, presuppositions, verb semantics and framesExamples from a wider range of languages that include German, Japanese, Spanish and Russian.Practical exercises on linguistic dataCompanion website including all figures and tables from the book, an online dictionary, answers to the exercises and useful links at routledge.com/cw/loebner This book is an essential resource for all undergraduate students studying semantics. Sebastian Löbner is a Professor of Linguistics at the Institute for Language and Information at the University of Düsseldorf, Germany
R 2,855.63
Semantics
This monograph provides a synthesis of the author's theory of meaning, which is based on conceptual primitives and semantic universals, using empirical findings to form a wide range of languages. While addressed primarily at linguists, the book deals with highly topical and controversial issues of central importance to several other disciplines, including anthropology, psychology and philosophy.
R 3,597.00
Semantics
This practical coursebook introduces all the basics of semantics in a simple, step-by-step fashion. Each unit includes short sections of explanation with examples, followed by stimulating practice exercises to complete in the book. Feedback and comment sections follow each exercise to enable students to monitor their progress. No previous background in semantics is assumed, as students begin by discovering the value and fascination of the subject and then move through all key topics in the field, including sense and reference, simple logic, word meaning and interpersonal meaning. New study guides and exercises have been added to the end of each unit to help reinforce and test learning. A completely new unit on non-literal language and metaphor, plus updates throughout the text significantly expand the scope of the original edition to bring it up-to-date with modern teaching of semantics for introductory courses in linguistics as well as intermediate students.
R 1,756.95
Semantics
When the first edition of Semantics appeared in 1976, the developments in this aspect of language study were exciting interest not only among linguists, but among philosophers, psychologists and logicians. Professor Palmer's straightforward and comprehensive book was immediately welcomed as one of the best introductions to the subject. Interest in Semantics has been further stimulated recently by a number of significant, and often contriversial, theoretical advances; and the publication of this second edition has enabled Professor Palmer to bring his survey thoroughly up to date. There is also an important new chapter on 'Semantics and logic', showing clearly and simply the influence that logical models have had on the study of meaning. Professor Palmer always illustrates his argument with helpful examples, and his non-technical explanations will be readily intelligible to the interested layman as well as to beginning students of language and linguistics.
R 3,224.76
Semantics
Semantics is an accessible and practical introduction to formal semantics, the study of linguistic meaning, for students new to the subject. Semantics:* shows how meanings are built up and interrelated* presupposes very little prior knowledge of grammar or linguistic terminology * includes a glossary of technical terms* provides a progression of exercises with answers given at the back * backs up the activities with short, clear explanations* includes an appendix on sets and functions.
R 7,590.05
Semantics
This text is an introduction to formal semantics - the study of linguistic meaning - for students. It shows how meanings are built up and interrelated, presupposes very little prior knowledge of grammar or linguistic terminology and includes a glossary of technical terms. It also provides exercises with answers, backs up the activities with short explanations and includes an appendix on sets and functions.
R 1,778.31
Deep Learning: Concepts and Architectures
This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm supports automatic mechanisms of structural knowledge representation. It discusses a number of multilayer architectures giving rise to tangible and functionally meaningful pieces of knowledge, and shows how the structural developments have become essential to the successful delivery of competitive practical solutions to real-world problems. The book also demonstrates how the architectural developments, which arise in the setting of deep learning, support detailed learning and refinements to the system design. Featuring detailed descriptions of the current trends in the design and analysis of deep learning topologies, the book offers practical guidelines and presents competitive solutions to various areas of language modeling, graph representation, and forecasting.
R 6,594.00
Deep Learning
DEEP LEARNING A concise and practical exploration of key topics and applications in data science In Deep Learning: From Big Data to Artificial Intelligence with R, expert researcher Dr. Stéphane Tufféry delivers an insightful discussion of the applications of deep learning and big data that focuses on practical instructions on various software tools and deep learning methods relying on three major libraries: MXNet, PyTorch, and Keras-TensorFlow. In the book, numerous, up-to-date examples are combined with key topics relevant to modern data scientists, including processing optimization, neural network applications, natural language processing, and image recognition. This is a thoroughly revised and updated edition of a book originally released in French, with new examples and methods included throughout. Classroom-tested and intuitively organized, Deep Learning: From Big Data to Artificial Intelligence with R offers complimentary access to a companion website that provides R and Python source code for the examples offered in the book. Readers will also find: A thorough introduction to practical deep learning techniques with explanations and examples for various programming librariesComprehensive explorations of a variety of applications for deep learning, including image recognition and natural language processingDiscussions of the theory of deep learning, neural networks, and artificial intelligence linked to concrete techniques and strategies commonly used to solve real-world problems Perfect for graduate students studying data science, big data, deep learning, and artificial intelligence, Deep Learning: From Big Data to Artificial Intelligence with R will also earn a place in the libraries of data science researchers and practicing data scientists.
R 3,257.00
Practical Deep Learning, 2nd Edition
If you've been curious about artificial intelligence and machine learning but didn't know where to start, this is the book you've been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning, 2nd Edition teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math - the book will cover the rest. After an introduction to Python, you'll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models' performance. You'll also learn: How to use classic machine learning models like k-Nearest Neighbours, Random Forests, and Support Vector Machines, How neural networks work and how they're trained, How to use convolutional neural networks, How to develop a successful deep learning model from scratch. You'll conduct experiments along the way, building to a final case study that incorporates everything you've learned. This second edition is thoroughly revised and updated, and adds six new chapters to further your exploration of deep learning from basic CNNs to more advanced models. New chapters cover fine tuning, transfer learning, object detection, semantic segmentation, multilabel classification, self-supervised learning, generative adversarial networks, and large language models. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning, 2nd Edition will give you the skills and confidence to dive into your own machine learning projects.
R 1,596.00
Deep Learning: Concepts and Architectures
This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm supports automatic mechanisms of structural knowledge representation. It discusses a number of multilayer architectures giving rise to tangible and functionally meaningful pieces of knowledge, and shows how the structural developments have become essential to the successful delivery of competitive practical solutions to real-world problems. The book also demonstrates how the architectural developments, which arise in the setting of deep learning, support detailed learning and refinements to the system design. Featuring detailed descriptions of the current trends in the design and analysis of deep learning topologies, the book offers practical guidelines and presents competitive solutions to various areas of language modeling, graph representation, and forecasting.
R 7,706.79
Default Semantics
In this pioneering book Kasia Jaszczolt lays down the foundations of an original theory of meaning in discourse, reveals the cognitive foundations of discourse interpretation, and puts forward a new basis for the analysis of discourse processing. She provides a step-by-step introduction to the theory and its application, and explains new terms and formalisms as required. Dr Jaszczolt unites the precision of truth-conditional, dynamic approaches with insights fromneo-Gricean pragmatics into the role of speaker's intentions in communication. She shows that the compositionality of meaning may be understood as merger representations combining information from various sources including word meaning and sentence structure, various kinds of default interpretations,and conscious pragmatic inference. Among the applications the author discusses are constructions that pose problems in semantic analysis such as referring expressions, propositional attitude constructions, presupposition, modality, numerals, and sentential connectives. She proposes solutions to cutting edge problems in the semantics/pragmatics interface - for example, how many levels of meaning should be distinguished; the status of under-specification; how much contextual information should be placed in the representation ofthe speaker's meaning; whether there are default interpretations; the stage of utterance interpretation at which pragmatic inference begins; and whether compositionality is a necessary feature of the theory of meaning and if so how it is to be defined.The book is for advanced students and researchers in semantics, pragmatics, computational linguistics, and philosophy of language.
R 2,978.00
Deep Learning for Multimedia Processing Applications
Deep Learning for Multimedia Processing is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing. Written for a wide range of readers, from students to professionals, this book offers a concise and accessible overview of the application of deep learning in various multimedia domains, including image processing, video analysis, audio recognition, and natural language processing.Divided into two volumes, Volumes Two delves into advanced topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), explaining their unique capabilities in multimedia tasks. Readers will discover how deep learning techniques enable accurate and efficient image recognition, object detection, semantic segmentation, and image synthesis. The book also covers video analysis techniques, including action recognition, video captioning, and video generation, highlighting the role of deep learning in extracting meaningful information from videos.Furthermore, the book explores audio processing tasks such as speech recognition, music classification, and sound event detection using deep learning models. It demonstrates how deep learning algorithms can effectively process audio data, opening up new possibilities in multimedia applications. Lastly, the book explores the integration of deep learning with natural language processing techniques, enabling systems to understand, generate, and interpret textual information in multimedia contexts.Throughout the book, practical examples, code snippets, and real-world case studies are provided to help readers gain hands-on experience in implementing deep learning solutions for multimedia processing. Deep Learning for Multimedia Processing is an essential resource for anyone interested in harnessing the power of deep learning to unlock the vast potential of multimedia data.
R 6,118.00
Deep Learning
This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition.
R 6,095.09
Deep Semantics and the Evolution of New Scientific Theories and Discoveries
Combining a variety of sounds to form words that can be understood by other individuals, language is one of the defining characteristics of the human species. However, since even highly educated people, great writers, and poets are not consistent regarding the meanings of words, we are unlikely to find consistent rules regarding word meanings by examining human language use. Therefore, deep semantics aims to study of the meanings of individual sounds and their role in creating the meanings of words.Deep Semantics and the Evolution of New Scientific Theories and Discoveries provides innovative insights into the mental processing of word meanings and lack of consistency in human use, while providing examples from different language sources such as, the Quran and Arabic text. This publication presents word roots, the human cognitive system, sound function, and knowledge process, and is designed for linguists, educators, speech professionals, researchers, students, and academics whose interests include topics on the study of people’s imperfect views, feelings, and habits in using words.
R 7,171.60
Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics
Data science revolves around two giants: Big Data analytics and Deep Learning. It is becoming challenging to handle and retrieve useful information due to how fast data is expanding. This book presents the technologies and tools to simplify and streamline the formation of Big Data as well as Deep Learning systems. This book discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and decision-making. It also covers numerous applications in healthcare, education, communication, media, and entertainment. Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics offers innovative platforms for integrating Big Data and Deep Learning and presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval. FEATURES Provides insight into the skill set that leverages one’s strength to act as a good data analyst Discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and help in decision-making Covers numerous potential applications in healthcare, education, communication, media, and entertainment Offers innovative platforms for integrating Big Data and Deep Learning Presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval from Big DataThis book is aimed at industry professionals, academics, research scholars, system modelers, and simulation experts.
R 7,567.77
Deep Learning
Although the ability to retain, process, and project prior experience onto future situations is indispensable, the human mind also possesses the ability to override experience and adapt to changing circumstances. Cognitive scientist Stellan Ohlsson analyzes three types of deep, non-monotonic cognitive change: creative insight, adaptation of cognitive skills by learning from errors, and conversion from one belief to another, incompatible belief. For each topic, Ohlsson summarizes past research, re-formulates the relevant research questions, and proposes information-processing mechanisms that answer those questions. The three theories are based on the principles of redistribution of activation, specialization of practical knowledge, and re-subsumption of declarative information. Ohlsson develops the implications of those mechanisms by scaling their effects with respect to time, complexity, and social interaction. The book ends with a unified theory of non-monotonic cognitive change that captures the abstract properties that the three types of change share.
R 2,807.76
Deep Learning
Deep Learning: Foundations and Concepts aims to offer both newcomers to machine learning and those already experienced in the field a comprehensive grasp of fundamental ideas underpinning deep learning. Covering key concepts related to contemporary deep learning architectures and techniques, this essential book will equip readers with a robust foundation for potential future specialization. The field of deep learning is undergoing rapid evolution. Rather than summarizing the latest research developments, Bishop distills the key ideas in order to ensure that the foundations and concepts presented in this book will endure the test of time. For enhanced accessibility, the book is organized into numerous bite-sized chapters, each exploring a distinct topic. The narrative follows a linear progression, with each chapter building upon content from its predecessors. This structure lends itself effectively to teaching a two-semester undergraduate or postgraduate machine learning course, while remaining equally relevant to those engaged in active research or in self-study.To fully grasp machine learning, a certain level of mathematical understanding is required. The book provides a self-contained introduction to probability theory, and includes appendices summarizing useful results in linear algebra, calculus of variations, and Lagrange multipliers. However, the focus of the book is on conveying a clear understanding of ideas rather than mathematical rigor, with emphasis on real-world practical value of techniques rather than abstract theory. Complex concepts are presented from multiple perspectives including textual descriptions, diagrams, mathematical formulae, and pseudo-code to cater to readers from diverse backgrounds.This book can be viewed as a successor to Neural Networks for Pattern Recognition (Bishop, 1995a) which provided the first comprehensive treatment of neural networks from a statistical perspective. It can be considered as a companion volume to Pattern Recognition and Machine Learning (Bishop, 2006) which covered a broader range of topics in machine learning but predates the deep learning revolution.
R 3,297.00
Semantics with Assignment Variables
This pioneering study combines insights from philosophy and linguistics to develop a novel framework for theorizing about linguistic meaning and the role of context in interpretation. A key innovation is to introduce explicit representations of context - assignment variables - in the syntax and semantics of natural language. The proposed theory systematizes a spectrum of 'shifting' phenomena in which the context relevant for interpreting certain expressions depends on features of the linguistic environment. Central applications include local and non-local contextual dependencies with quantifiers, attitude ascriptions, conditionals, questions, and relativization. The result is an innovative philosophically informed compositional semantics compatible with the truth-conditional paradigm. At the forefront of contemporary interdisciplinary research into meaning and communication, Semantics with Assignment Variables is essential reading for researchers and students in a diverse range of fields.
R 5,279.22
Deep Learning
An engaging and accessible introduction to deep learning perfect for students and professionals In Deep Learning: A Practical Introduction, a team of distinguished researchers delivers a start-to-finish instruction book with complete coverage of the theoretical and practical elements of deep learning. The book includes extensive examples, end-of-chapter exercises, homework, exam material and a GitHub repository containing code and data for all provided examples. Combining contemporary deep learning theory with state-of-the-art tools, the chapters are structured to maximize accessibility for both beginning and intermediate students. The authors have included coverage of TensorFlow, Keras, and Pytorch. Readers will also find: Thorough introductions to deep learning and deep learning toolsComprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architecturesPractical discussions of recurrent neural networks and non-supervised approaches to deep learningFulsome treatments of generative adversarial networks as well as deep Bayesian Neural networks.Perfect for undergraduate and graduate students studying computer vision, computer science, artificial intelligence, and neural networks, Deep Learning: A Practical Introduction will also benefit practitioners and researchers in the fields of deep learning and machine learning in general.
R 3,237.00
R 4,732.00
