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R 3,053.00
Concept Drift in Large Language Models
This book explores the application of the complex relationship between concept drift and cutting-edge large language models to address the problems and opportunities in navigating changing data landscapes. It discusses the theoretical basis of concept drift and its consequences for large language models, particularly the transformative power of cutting-edge models such as GPT-3.5 and GPT-4. It offers real-world case studies to observe firsthand how concept drift influences the performance of language models in a variety of circumstances, delivering valuable lessons learnt and actionable takeaways. The book is designed for professionals, AI practitioners, and scholars, focused on natural language processing, machine learning, and artificial intelligence.Examines concept drift in AI, particularly its impact on large language modelsAnalyses how concept drift affects large language models and its theoretical and practical consequencesCovers detection methods and practical implementation challenges in language modelsShowcases examples of concept drift in GPT models and lessons learnt from their performanceIdentifies future research avenues and recommendations for practitioners tackling concept drift in large language models
R 5,613.19
R 489.00
Generative AI and Large Language Models: Opportunities, Challenges, and Applications
This book provides a comprehensive exploration of the transformative impact of AI technologies across diverse fields. From revolutionizing healthcare diagnostics and advancing natural language processing for low-resource languages to enhancing software development and promoting environmental sustainability, this book explores the cutting-edge advancements and practical applications of generative AI and large language models (LLMs). With a focus on both opportunities and challenges, the book examines the architectural challenges of transformer-based models, the ethical implications of AI, and the importance of language-specific adaptations, particularly for low-resource languages like Arabic. It also highlights the role of AI in code development, multimodal applications, and its integration with intellectual property frameworks. This book is an essential resource for researchers, practitioners, and policymakers seeking to understand and harness the potential of AI to drive innovation and global progress.
R 8,387.99
Privacy and Security for Large Language Models
As the deployment of AI technologies surges, the need to safeguard privacy and security in the use of large language models (LLMs) is more crucial than ever. Professionals face the challenge of leveraging the immense power of LLMs for personalized applications while ensuring stringent data privacy and security. The stakes are high, as privacy breaches and data leaks can lead to significant reputational and financial repercussions. This book serves as a much-needed guide to addressing these pressing concerns. Dr. Baihan Lin offers a comprehensive exploration of privacy-preserving and security techniques like differential privacy, federated learning, and homomorphic encryption, applied specifically to LLMs. With its hands-on code examples, real-world case studies, and robust fine-tuning methodologies in domain-specific applications, this book is a vital resource for developing secure, ethical, and personalized AI solutions in today's privacy-conscious landscape. By reading this book, you'll: Discover privacy-preserving techniques for LLMsLearn secure fine-tuning methodologies for personalizing LLMsUnderstand secure deployment strategies and protection against attacksExplore ethical considerations like bias and transparencyGain insights from real-world case studies across healthcare, finance, and moreExamine the legal and cultural landscape of AI deployment
R 1,978.00
Pretrain Vision and Large Language Models in Python
Master the art of training vision and large language models with conceptual fundaments and industry-expert guidance. Learn about AWS services and design patterns, with relevant coding examplesKey FeaturesLearn to develop, train, tune, and apply foundation models with optimized end-to-end pipelinesExplore large-scale distributed training for models and datasets with AWS and SageMaker examplesEvaluate, deploy, and operationalize your custom models with bias detection and pipeline monitoringBook DescriptionFoundation models have forever changed machine learning. From BERT to ChatGPT, CLIP to Stable Diffusion, when billions of parameters are combined with large datasets and hundreds to thousands of GPUs, the result is nothing short of record-breaking. The recommendations, advice, and code samples in this book will help you pretrain and fine-tune your own foundation models from scratch on AWS and Amazon SageMaker, while applying them to hundreds of use cases across your organization.With advice from seasoned AWS and machine learning expert Emily Webber, this book helps you learn everything you need to go from project ideation to dataset preparation, training, evaluation, and deployment for large language, vision, and multimodal models. With step-by-step explanations of essential concepts and practical examples, you’ll go from mastering the concept of pretraining to preparing your dataset and model, configuring your environment, training, fine-tuning, evaluating, deploying, and optimizing your foundation models.You will learn how to apply the scaling laws to distributing your model and dataset over multiple GPUs, remove bias, achieve high throughput, and build deployment pipelines.By the end of this book, you’ll be well equipped to embark on your own project to pretrain and fine-tune the foundation models of the future.What you will learnFind the right use cases and datasets for pretraining and fine-tuningPrepare for large-scale training with custom accelerators and GPUsConfigure environments on AWS and SageMaker to maximize performanceSelect hyperparameters based on your model and constraintsDistribute your model and dataset using many types of parallelismAvoid pitfalls with job restarts, intermittent health checks, and moreEvaluate your model with quantitative and qualitative insightsDeploy your models with runtime improvements and monitoring pipelinesWho this book is forIf you’re a machine learning researcher or enthusiast who wants to start a foundation modelling project, this book is for you. Applied scientists, data scientists, machine learning engineers, solution architects, product managers, and students will all benefit from this book. Intermediate Python is a must, along with introductory concepts of cloud computing. A strong understanding of deep learning fundamentals is needed, while advanced topics will be explained. The content covers advanced machine learning and cloud techniques, explaining them in an actionable, easy-to-understand way.
R 1,693.00
R 1,921.00
Challenges in Large Language Model Development and AI Ethics
The development of large language models has resulted in artificial intelligence advancements promising transformations and benefits across various industries and sectors. However, this progress is not without its challenges. The scale and complexity of these models pose significant technical hurdles, including issues related to bias, transparency, and data privacy. As these models integrate into decision-making processes, ethical concerns about their societal impact, such as potential job displacement or harmful stereotype reinforcement, become more urgent. Addressing these challenges requires a collaborative effort from business owners, computer engineers, policymakers, and sociologists. Fostering effective research for solutions to address AI ethical challenges may ensure that large language model developments benefit society in a positive way. Challenges in Large Language Model Development and AI Ethics addresses complex ethical dilemmas and challenges of the development of large language models and artificial intelligence. It analyzes ethical considerations involved in the design and implementation of large language models, while exploring aspects like bias, accountability, privacy, and social impacts. This book covers topics such as law and policy, model architecture, and machine learning, and is a useful resource for computer engineers, sociologists, policymakers, business owners, academicians, researchers, and scientists.
R 12,942.10
Large Language Models for Automatic Deidentification of Electronic Health Record Notes
This volume constitutes the refereed proceedings of the International Workshop on Deidentification of Electronic Health Record Notes, IW-DMRN 2024, held on January 15, 2024, in Kaohsiung, Taiwan.The 15 full papers were carefully reviewed and selected from 30 submissions. The conference focuses on medical data analysis, enhancing medication safety, and optimizing medical care efficiency.
R 3,114.00
Developer's Playbook for Large Language Model Security
Large language models (LLMs) are not just shaping the trajectory of AI, they're also unveiling a new era of security challenges. This practical book takes you straight to the heart of these threats. Author Steve Wilson, chief product officer at Exabeam, focuses exclusively on LLMs, eschewing generalized AI security to delve into the unique characteristics and vulnerabilities inherent in these models.Complete with collective wisdom gained from the creation of the OWASP Top 10 for LLMs list—a feat accomplished by more than 400 industry experts—this guide delivers real-world guidance and practical strategies to help developers and security teams grapple with the realities of LLM applications. Whether you're architecting a new application or adding AI features to an existing one, this book is your go-to resource for mastering the security landscape of the next frontier in AI.You'll learn:Why LLMs present unique security challengesHow to navigate the many risk conditions associated with using LLM technologyThe threat landscape pertaining to LLMs and the critical trust boundaries that must be maintainedHow to identify the top risks and vulnerabilities associated with LLMsMethods for deploying defenses to protect against attacks on top vulnerabilitiesWays to actively manage critical trust boundaries on your systems to ensure secure execution and risk minimization
R 1,824.00
Natural Language Processing
This textbook provides a contemporary and comprehensive overview of Natural Language Processing (NLP), covering fundamental concepts, core algorithms, and key applications such as AI chatbots, Large Language Models and Generative AI. Additionally, it includes seven step-by-step NLP workshops, totaling 14 hours, that offer hands-on practice with essential Python tools, including NLTK, spaCy, TensorFlow, Keras, Transformers, and BERT. The objective of this book is to provide readers with a fundamental grasp of NLP and its core technologies, and to enable them to build their own NLP applications (e.g. Chatbot systems) using Python-based NLP tools. It is both a textbook and NLP tool-book intended for the following readers: undergraduate students from various disciplines who want to learn NLP; lecturers and tutors who want to teach courses or tutorials for undergraduate/graduate students on NLP and related AI topics; and readers with various backgrounds who want to learn NLP, and more importantly, to build workable NLP applications after completing its 14 hours of Python-based workshops.
R 3,297.00
Foundation Models for Natural Language Processing
This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models. After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI.
R 1,832.00
Foundation Models for Natural Language Processing
This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models. After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI.
R 2,198.00
Language Modeling for Automatic Speech Recognition of Inflective Languages
This book covers language modeling and automatic speech recognition for inflective languages (e.g. Slavic languages), which represent roughly half of the languages spoken in Europe. These languages do not perform as well as English in speech recognition systems and it is therefore harder to develop an application with sufficient quality for the end user. The authors describe the most important language features for the development of a speech recognition system. This is then presented through the analysis of errors in the system and the development of language models and their inclusion in speech recognition systems, which specifically address the errors that are relevant for targeted applications. The error analysis is done with regard to morphological characteristics of the word in the recognized sentences. The book is oriented towards speech recognition with large vocabularies and continuous and even spontaneous speech. Today such applications work with a rather small number of languages compared to the number of spoken languages.
R 2,398.45
R 1,008.00
Models of Language Acquisition
This work presents advances by leading researchers in the computational modelling of language acquisition. Sophisticated theoretical models can now be tested using simulation techniques and large corpora of linguistic data. Renewed interest in learning neural networks and the ability to test new solutions to fundamental problems has fuelled debates in an already very active field The 24 authors in this collection have been drawn from departments of linguistics, cognitive science, psychology, and computer science. The book as a whole shows what light may be thrown on fundamental problems when powerful computational techniques are combined with real data. A central question addressed in the book concerns the extent to which linguistic structure is readily available in the environment. The authors consider the evidence in relation to word boundaries and phonotactic structure, stress patterns, text-to-speech rules, and the mapping of lexical semantics, one author arguing that a child's own output may serve as a key source of linguistic input.
R 2,669.00
Natural Language Processing and Chinese Computing
The four-volume set LNAI 16102 - 16105 constitutes the refereed proceedings of the 14th CCF National Conference on Natural Language Processing and Chinese Computing, NLPCC 2025, held in Urumqi, China, during August 7-9, 2025. The 152 full papers and 26 evaluation workshop papers included in these proceedings were carefully reviewed and selected from 505 submissions. They were focused on the following topical sections: Part I: Information Extraction and Knowledge Graph & Large Language Models and Agents.Part II: Multimodality and Explainability & NLP Applications / Text Mining.Part III: IR / Dialogue Systems / Question Answering; Machine Translation and Multilinguality & Sentiment analysis / Argumentation Mining / Social Media.Part IV: Machine Learning for NLP; Fundamentals of NLP; Summarization and Generation; Others &Evaluation Workshop.
R 4,329.96
Coarse-to-Fine Natural Language Processing
The impact of computer systems that can understand natural language will be tremendous. To develop this capability we need to be able to automatically and efficiently analyze large amounts of text. Manually devised rules are not sufficient to provide coverage to handle the complex structure of natural language, necessitating systems that can automatically learn from examples. To handle the flexibility of natural language, it has become standard practice to use statistical models, which assign probabilities for example to the different meanings of a word or the plausibility of grammatical constructions.This book develops a general coarse-to-fine framework for learning and inference in large statistical models for natural language processing.Coarse-to-fine approaches exploit a sequence of models which introduce complexity gradually. At the top of the sequence is a trivial model in which learning and inference are both cheap. Each subsequent model refines the previous one, until a final, full-complexity model is reached. Applications of this framework to syntactic parsing, speech recognition and machine translation are presented, demonstrating the effectiveness of the approach in terms of accuracy and speed. The book is intended for students and researchers interested in statistical approaches to Natural Language Processing. Slav’s work Coarse-to-Fine Natural Language Processing represents a major advance in the area of syntactic parsing, and a great advertisement for the superiority of the machine-learning approach.Eugene Charniak (Brown University)
R 3,426.79
Statistical Language Models for Information Retrieval
As online information grows dramatically, search engines such as Google are playing a more and more important role in our lives. Critical to all search engines is the problem of designing an effective retrieval model that can rank documents accurately for a given query. This has been a central research problem in information retrieval for several decades. In the past ten years, a new generation of retrieval models, often referred to as statistical language models, has been successfully applied to solve many different information retrieval problems. Compared with the traditional models such as the vector space model, these new models have a more sound statistical foundation and can leverage statistical estimation to optimize retrieval parameters. They can also be more easily adapted to model non-traditional and complex retrieval problems. Empirically, they tend to achieve comparable or better performance than a traditional model with less effort on parameter tuning. This book systematically reviews the large body of literature on applying statistical language models to information retrieval with an emphasis on the underlying principles, empirically effective language models, and language models developed for non-traditional retrieval tasks. All the relevant literature has been synthesized to make it easy for a reader to digest the research progress achieved so far and see the frontier of research in this area. The book also offers practitioners an informative introduction to a set of practically useful language models that can effectively solve a variety of retrieval problems. No prior knowledge about information retrieval is required, but some basic knowledge about probability and statistics would be useful for fully digesting all the details.Table of Contents: Introduction / Overview of Information Retrieval Models / Simple Query Likelihood Retrieval Model / Complex Query Likelihood Model / Probabilistic Distance Retrieval Model / Language Models for Special Retrieval Tasks / Language Models for Latent Topic Analysis / Conclusions
R 1,508.53
Natural Language Processing and Chinese Computing
This three-volume set constitutes the refereed proceedings of the 12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023, held in Foshan, China, during October 12–15, 2023. The 143 regular papers included in these proceedings were carefully reviewed and selected from 478 submissions. They were organized in topical sections as follows: dialogue systems; fundamentals of NLP; information extraction and knowledge graph; machine learning for NLP; machine translation and multilinguality; multimodality and explainability; NLP applications and text mining; question answering; large language models; summarization and generation; student workshop; and evaluation workshop.
R 5,136.16
Building Personality-Driven Language Models
This book provides an innovative exploration into the realm of artificial intelligence (AI) by developing personalities for large language models (LLMs) using psychological principles. Aimed at making AI interactions feel more human-like, the book guides you through the process of applying psychological assessments to AIs, enabling them to exhibit traits such as extraversion, openness, and emotional stability. Perfect for developers, researchers, and entrepreneurs, this work merges psychology, philosophy, business, and cutting-edge computing to enhance how AIs understand and engage with humans across various industries like gaming and healthcare. The book not only unpacks the theoretical aspects of these advancements but also equips you with practical coding exercises and Python code examples, helping you create AI systems that are both innovative and relatable. Whether you’re looking to deepen your understanding of AI personalities or integrate them into commercial applications, this book offers the tools and insights needed to pioneer this exciting frontier.
R 1,832.00
Natural Language Processing and Chinese Computing
The four-volume set LNAI 16102 - 16105 constitutes the refereed proceedings of the 14th CCF National Conference on Natural Language Processing and Chinese Computing, NLPCC 2025, held in Urumqi, China, during August 7-9, 2025. The 152 full papers and 26 evaluation workshop papers included in these proceedings were carefully reviewed and selected from 505 submissions. They were focused on the following topical sections: Part I: Information Extraction and Knowledge Graph & Large Language Models and Agents.Part II: Multimodality and Explainability & NLP Applications / Text Mining.Part III: IR / Dialogue Systems / Question Answering; Machine Translation and Multilinguality & Sentiment analysis / Argumentation Mining / Social Media.Part IV: Machine Learning for NLP; Fundamentals of NLP; Summarization and Generation; Others &Evaluation Workshop.
R 4,328.50
Natural Language Processing with TensorFlow
From introductory NLP tasks to Transformer models, this new edition teaches you to utilize powerful TensorFlow APIs to implement end-to-end NLP solutions driven by performant ML (Machine Learning) modelsKey FeaturesLearn to solve common NLP problems effectively with TensorFlow 2.xImplement end-to-end data pipelines guided by the underlying ML model architectureUse advanced LSTM techniques for complex data transformations, custom models and metricsBook DescriptionLearning how to solve natural language processing (NLP) problems is an important skill to master due to the explosive growth of data combined with the demand for machine learning solutions in production. Natural Language Processing with TensorFlow, Second Edition, will teach you how to solve common real-world NLP problems with a variety of deep learning model architectures. The book starts by getting readers familiar with NLP and the basics of TensorFlow. Then, it gradually teaches you different facets of TensorFlow 2.x. In the following chapters, you then learn how to generate powerful word vectors, classify text, generate new text, and generate image captions, among other exciting use-cases of real-world NLP. TensorFlow has evolved to be an ecosystem that supports a machine learning workflow through ingesting and transforming data, building models, monitoring, and productionization. We will then read text directly from files and perform the required transformations through a TensorFlow data pipeline. We will also see how to use a versatile visualization tool known as TensorBoard to visualize our models. By the end of this NLP book, you will be comfortable with using TensorFlow to build deep learning models with many different architectures, and efficiently ingest data using TensorFlow Additionally, you'll be able to confidently use TensorFlow throughout your machine learning workflow.What you will learnLearn core concepts of NLP and techniques with TensorFlowUse state-of-the-art Transformers and how they are used to solve NLP tasksPerform sentence classification and text generation using CNNs and RNNsUtilize advanced models for machine translation and image caption generationBuild end-to-end data pipelines in TensorFlowLearn interesting facts and practices related to the task at handCreate word representations of large amounts of data for deep learningWho this book is forThis book is for Python developers and programmers with a strong interest in deep learning, who want to learn how to leverage TensorFlow to simplify NLP tasks.Fundamental Python skills are assumed, as well as basic knowledge of machine learning and undergraduate-level calculus and linear algebra. No previous natural language processing experience required.
R 1,433.00
Models of Language Acquisition
This volume presents advances by leading researchers in computational modelling of language acquisition. Sophisticated theoretical models can now be tested using simulation techniques and large corpora of linguistic data. Renewed interest in learning neural networks and the ability to test new solutions to fundamental problems has fuelled debates in an already very active field. The 24 authors in this collection have been drawn from departments of linguistics, cognitive science, psychology, and computer science. The book as a whole shows what light may be thrown on fundamental problems when powerful computational techniques are combined with real data. A central question addressed in the book concerns the extent to which linguistic structure is readily available in the environment. The authors consider the evidence in relation to word boundaries and phonotactic structure, stress patterns, text-to-speech rules, and the mapping of lexical semantics, one author arguing that a child's own output may serve as a key source of linguistic input.
R 10,827.00
Natural Language Processing and Chinese Computing
The four-volume set LNAI 16102 - 16105 constitutes the refereed proceedings of the 14th CCF National Conference on Natural Language Processing and Chinese Computing, NLPCC 2025, held in Urumqi, China, during August 7-9, 2025. The 152 full papers and 26 evaluation workshop papers included in these proceedings were carefully reviewed and selected from 505 submissions. They were focused on the following topical sections: Part I: Information Extraction and Knowledge Graph & Large Language Models and Agents.Part II: Multimodality and Explainability & NLP Applications / Text Mining.Part III: IR / Dialogue Systems / Question Answering; Machine Translation and Multilinguality & Sentiment analysis / Argumentation Mining / Social Media.Part IV: Machine Learning for NLP; Fundamentals of NLP; Summarization and Generation; Others &Evaluation Workshop.
R 4,317.03
R 842.00
Reinforcement Learning Methods in Speech and Language Technology
This book offers a comprehensive guide to reinforcement learning (RL) and bandits methods, specifically tailored for advancements in speech and language technology. Starting with a foundational overview of RL and bandit methods, the book dives into their practical applications across a wide array of speech and language tasks. Readers will gain insights into how these methods shape solutions in automatic speech recognition (ASR), speaker recognition, diarization, spoken and natural language understanding (SLU/NLU), text-to-speech (TTS) synthesis, natural language generation (NLG), and conversational recommendation systems (CRS). Further, the book delves into cutting-edge developments in large language models (LLMs) and discusses the latest strategies in RL, highlighting the emerging fields of multi-agent systems and transfer learning.Emphasizing real-world applications, the book provides clear, step-by-step guidance on employing RL and bandit methods to address challenges in speech and language technology. It includes case studies and practical tips that equip readers to apply these methods to their own projects. As a timely and crucial resource, this book is ideal for speech and language researchers, engineers, students, and practitioners eager to enhance the performance of speech and language systems and to innovate with new interactive learning paradigms from an interface design perspective.
R 4,030.00
R 1,529.00
R 1,136.00
Understanding Natural Language Understanding
About half a century ago, AI pioneers like Marvin Minsky embarked on the ambitious project of emulating how the human mind encodes and decodes meaning. While today we have a better understanding of the brain thanks to neuroscience, we are still far from unlocking the secrets of the mind, especially when it comes to language, the prime example of human intelligence. “Understanding natural language understanding”, i.e., understanding how the mind encodes and decodes meaning through language, is a significant milestone in our journey towards creating machines that genuinely comprehend human language. Large language models (LLMs) such as GPT-4 have astounded us with their ability to generate coherent, contextually relevant text, seemingly bridging the gap between human and machine communication. Yet, despite their impressive capabilities, these models operate on statistical patterns rather than true comprehension. This textbook delves into the nuanced differences between these two paradigms and explores the future of AI as we strive to achieve true natural language understanding (NLU). LLMs excel at identifying and replicating patterns within vast datasets, producing responses that appear intelligent and meaningful. They can generate text that mimics human writing styles, provide summaries of complex documents, and even engage in extended dialogues with users. However, their limitations become evident when they encounter tasks that require deeper understanding, reasoning, and contextual knowledge. An NLU system that deconstructs meaning leveraging linguistics and semiotics (on top of statistical analysis) represents a more profound level of language comprehension. It involves understanding context in a manner similar to human cognition, discerning subtle meanings, implications, and nuances that current LLMs might miss or misinterpret. NLU grasps the semantics behind words and sentences, comprehending synonyms, metaphors, idioms, and abstract concepts with precision.This textbook explores the current state of LLMs, their capabilities and limitations, and contrasts them with the aspirational goals of NLU. The author delves into the technical foundations required for achieving true NLU, including advanced knowledge representation, hybrid AI systems, and neurosymbolic integration, while also examining the ethical implications and societal impacts of developing AI systems that genuinely understand human language. Containing exercises, a final assignment and a comprehensive quiz, the textbook is meant as a reference for courses on information retrieval, AI, NLP, data analytics, data mining and more.
R 3,663.00
Natural Language Processing and Chinese Computing
The four-volume set LNAI 16102 - 16105 constitutes the refereed proceedings of the 14th CCF National Conference on Natural Language Processing and Chinese Computing, NLPCC 2025, held in Urumqi, China, during August 7-9, 2025. The 152 full papers and 26 evaluation workshop papers included in these proceedings were carefully reviewed and selected from 505 submissions. They were focused on the following topical sections: Part I: Information Extraction and Knowledge Graph & Large Language Models and Agents.Part II: Multimodality and Explainability & NLP Applications / Text Mining.Part III: IR / Dialogue Systems / Question Answering; Machine Translation and Multilinguality & Sentiment analysis / Argumentation Mining / Social Media.Part IV: Machine Learning for NLP; Fundamentals of NLP; Summarization and Generation; Others &Evaluation Workshop.
R 3,174.57
R 2,209.23
Natural Language Understanding with Python
Build advanced Natural Language Understanding Systems by acquiring data and selecting appropriate technology.Key FeaturesMaster NLU concepts from basic text processing to advanced deep learning techniquesExplore practical NLU applications like chatbots, sentiment analysis, and language translationGain a deeper understanding of large language models like ChatGPTBook DescriptionNatural language understanding (NLU) organizes and structures, language allowing computer systems to effectively process textual information for many different practical applications. Natural Language Understanding with Python will help you explore practical techniques that make use of NLU to build a wide variety of creative and useful applications.Complete with step-by-step explanations of essential concepts and practical examples, this book begins by teaching you about NLU and its applications. You’ll then explore a wide range of current NLU techniques and their most appropriate use-case. In the process, you’ll be introduced to the most useful Python NLU libraries. Not only will you learn the basics of NLU, you’ll also be introduced to practical issues such as acquiring data, evaluating systems, and deploying NLU applications, along with their solutions. This book is a comprehensive guide that will help you explore the full spectrum of essential NLU techniques and resources.By the end of this book, you will be familiar with the foundational concepts of NLU, deep learning, and large language models (LLMs). You will be well on your way to having the skills to independently apply NLU technology in your own academic and practical applications.What you will learnThe most important skill that readers will acquire is not just HOW to apply natural language techniques, but WHY to select particular techniques.The book will also cover important practical considerations concerning acquiring real data and evaluating real system performance, not just performing textbook evaluations with pre-existing corpora.After reading this book and studying the code, readers will be equipped to build state of the art as well as practical natural language applications to solve real problems.How to develop and fine-tune an NLP applicationMaintaining NLP applications after deploymentWho this book is forThis book is for python developers, computational linguists, linguists, data scientists, NLP developers, conversational AI developers, and students looking to learn about natural language understanding (NLU) and applying natural language processing (NLP) technology to real problems. Anyone interested in addressing natural language problems will find this book useful. Working knowledge in Python is a must.
R 1,693.00
Python Natural Language Processing Cookbook
Updated to include three new chapters on transformers, natural language understanding (NLU) with explainable AI, and dabbling with popular LLMs from Hugging Face and OpenAIKey FeaturesLeverage ready-to-use recipes with the latest LLMs, including Mistral, Llama, and OpenAI modelsUse LLM-powered agents for custom tasks and real-world interactionsGain practical, in-depth knowledge of transformers and their role in implementing various NLP tasks with open-source and advanced LLMsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionHarness the power of Natural Language Processing to overcome real-world text analysis challenges with this recipe-based roadmap written by two seasoned NLP experts with vast experience transforming various industries with their NLP prowess.You’ll be able to make the most of the latest NLP advancements, including large language models (LLMs), and leverage their capabilities through Hugging Face transformers. Through a series of hands-on recipes, you’ll master essential techniques such as extracting entities and visualizing text data. The authors will expertly guide you through building pipelines for sentiment analysis, topic modeling, and question-answering using popular libraries like spaCy, Gensim, and NLTK. You’ll also learn to implement RAG pipelines to draw out precise answers from a text corpus using LLMs.This second edition expands your skillset with new chapters on cutting-edge LLMs like GPT-4, Natural Language Understanding (NLU), and Explainable AI (XAI)—fostering trust and transparency in your NLP models.By the end of this book, you'll be equipped with the skills to apply advanced text processing techniques, use pre-trained transformer models, build custom NLP pipelines to extract valuable insights from text data to drive informed decision-making.What you will learnUnderstand fundamental NLP concepts along with their applications using examples in PythonClassify text quickly and accurately with rule-based and supervised methodsTrain NER models and perform sentiment analysis to identify entities and emotions in textExplore topic modeling and text visualization to reveal themes and relationships within textLeverage Hugging Face and OpenAI LLMs to perform advanced NLP tasksUse question-answering techniques to handle both open and closed domainsApply XAI techniques to better understand your model predictionsWho this book is forThis updated edition of the Python Natural Language Processing Cookbook is for data scientists, machine learning engineers, and developers with a background in Python. Whether you’re looking to learn NLP techniques, extract valuable insights from textual data, or create foundational applications, this book will equip you with basic to intermediate skills. No prior NLP knowledge is necessary to get started. All you need is familiarity with basic programming principles. For seasoned developers, the updated sections offer the latest on transformers, explainable AI, and Gene
R 1,465.00
Modern Computational Models of Semantic Discovery in Natural Language
Language—that is, oral or written content that references abstract concepts in subtle ways—is what sets us apart as a species, and in an age defined by such content, language has become both the fuel and the currency of our modern information society. This has posed a vexing new challenge for linguists and engineers working in the field of language-processing: how do we parse and process not just language itself, but language in vast, overwhelming quantities?Modern Computational Models of Semantic Discovery in Natural Language compiles and reviews the most prominent linguistic theories into a single source that serves as an essential reference for future solutions to one of the most important challenges of our age. This comprehensive publication benefits an audience of students and professionals, researchers, and practitioners of linguistics and language discovery.This book includes a comprehensive range of topics and chapters covering digital media, social interaction in online environments, text and data mining, language processing and translation, and contextual documentation, among others.
R 8,329.35
Natural Language Processing for Corpus Linguistics
Corpus analysis can be expanded and scaled up by incorporating computational methods from natural language processing. This Element shows how text classification and text similarity models can extend our ability to undertake corpus linguistics across very large corpora. These computational methods are becoming increasingly important as corpora grow too large for more traditional types of linguistic analysis. We draw on five case studies to show how and why to use computational methods, ranging from usage-based grammar to authorship analysis to using social media for corpus-based sociolinguistics. Each section is accompanied by an interactive code notebook that shows how to implement the analysis in Python. A stand-alone Python package is also available to help readers use these methods with their own data. Because large-scale analysis introduces new ethical problems, this Element pairs each new methodology with a discussion of potential ethical implications.
R 865.00
