Produkt zum Begriff Natural Language Processing:
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Real-World Natural Language Processing
Voice assistants, automated customer service agents, and other cutting-edge human-to-computer interactions rely on accurately interpreting language as it is written and spoken. Real-world Natural Language Processing teaches you how to create practical NLP applications without getting bogged down in complex language theory and the mathematics of deep learning. In this engaging book, you’ll explore the core tools and techniques required to build a huge range of powerful NLP apps.about the technologyNatural language processing is the part of AI dedicated to understanding and generating human text and speech. NLP covers a wide range of algorithms and tasks, from classic functions such as spell checkers, machine translation, and search engines to emerging innovations like chatbots, voice assistants, and automatic text summarization. Wherever there is text, NLP can be useful for extracting meaning and bridging the gap between humans and machines.about the bookReal-world Natural Language Processing teaches you how to create practical NLP applications using Python and open source NLP libraries such as AllenNLP and Fairseq. In this practical guide, you’ll begin by creating a complete sentiment analyzer, then dive deep into each component to unlock the building blocks you’ll use in all different kinds of NLP programs. By the time you’re done, you’ll have the skills to create named entity taggers, machine translation systems, spelling correctors, and language generation systems. what's insideDesign, develop, and deploy basic NLP applicationsNLP libraries such as AllenNLP and FairseqAdvanced NLP concepts such as attention and transfer learningabout the readerAimed at intermediate Python programmers. No mathematical or machine learning knowledge required.about the authorMasato Hagiwara received his computer science PhD from Nagoya University in 2009, focusing on Natural Language Processing and machine learning. He has interned at Google and Microsoft Research, and worked at Baidu Japan, Duolingo, and Rakuten Institute of Technology. He now runs his own consultancy business advising clients, including startups and research institutions.
Preis: 58.84 € | Versand*: 0 € -
Multilingual Natural Language Processing Applications: From Theory to Practice
Multilingual Natural Language Processing Applications is the first comprehensive single-source guide to building robust and accurate multilingual NLP systems. Edited by two leading experts, it integrates cutting-edge advances with practical solutions drawn from extensive field experience. Part I introduces the core concepts and theoretical foundations of modern multilingual natural language processing, presenting today’s best practices for understanding word and document structure, analyzing syntax, modeling language, recognizing entailment, and detecting redundancy. Part II thoroughly addresses the practical considerations associated with building real-world applications, including information extraction, machine translation, information retrieval/search, summarization, question answering, distillation, processing pipelines, and more. This book contains important new contributions from leading researchers at IBM, Google, Microsoft, Thomson Reuters, BBN, CMU, University of Edinburgh, University of Washington, University of North Texas, and others. Coverage includes Core NLP problems, and today’s best algorithms for attacking them Processing the diverse morphologies present in the world’s languagesUncovering syntactical structure, parsing semantics, using semantic role labeling, and scoring grammaticalityRecognizing inferences, subjectivity, and opinion polarityManaging key algorithmic and design tradeoffs in real-world applications Extracting information via mention detection, coreference resolution, and eventsBuilding large-scale systems for machine translation, information retrieval, and summarizationAnswering complex questions through distillation and other advanced techniquesCreating dialog systems that leverage advances in speech recognition, synthesis, and dialog managementConstructing common infrastructure for multiple multilingual text processing applications This book will be invaluable for all engineers, software developers, researchers, and graduate students who want to process large quantities of text in multiple languages, in any environment: government, corporate, or academic.
Preis: 49.21 € | Versand*: 0 € -
Transfer Learning for Natural Processing
Building and training deep learning models from scratch is costly, time-consuming, and requires massive amounts of data. To address this concern, cutting-edge transfer learning techniques enable you to start with pretrained models you can tweak to meet your exact needs. In Transfer Learning for Natural Language Processing, DARPA researcher Paul Azunre takes you hands-on with customizing these open source resources for your own NLP architectures. You’ll learn how to use transfer learning to deliver state-of-the-art results even when working with limited label data, all while saving on training time and computational costs.about the technologyTransfer learning enables machine learning models to be initialized with existing prior knowledge. Initially pioneered in computer vision, transfer learning techniques have been revolutionising Natural Language Processing with big reductions in the training time and computation power needed for a model to start delivering results. Emerging pretrained language models such as ELMo and BERT have opened up new possibilities for NLP developers working in machine translation, semantic analysis, business analytics, and natural language generation.about the bookTransfer Learning for Natural Language Processing is a practical primer to transfer learning techniques capable of delivering huge improvements to your NLP models. Written by DARPA researcher Paul Azunre, this practical book gets you up to speed with the relevant ML concepts before diving into the cutting-edge advances that are defining the future of NLP. You’ll learn how to adapt existing state-of-the art models into real-world applications, including building a spam email classifier, a movie review sentiment analyzer, an automated fact checker, a question-answering system and a translation system for low-resource languages. what's insideFine tuning pretrained models with new domain dataPicking the right model to reduce resource usageTransfer learning for neural network architecturesFoundations for exploring NLP academic literatureabout the readerFor machine learning engineers and data scientists with some experience in NLP.about the authorPaul Azunre holds a PhD in Computer Science from MIT and has served as a Principal Investigator on several DARPA research programs. He founded Algorine Inc., a Research Lab dedicated to advancing AI/ML and identifying scenarios where they can have a significant social impact. Paul also co-founded Ghana NLP, an open source initiative focused using NLP and Transfer Learning with Ghanaian and other low-resource languages. He frequently contributes to major peer-reviewed international research journals and serves as a program committee member at top conferences in the field.
Preis: 49.21 € | Versand*: 0 € -
Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow
NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results"To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals."--From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA"Ekman uses a learning technique that in our experience has proven pivotal to successasking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us."--From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning InstituteDeep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience.After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images.Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning.Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagationSee how DL frameworks make it easier to develop more complicated and useful neural networksDiscover how convolutional neural networks (CNNs) revolutionize image classification and analysisApply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequencesMaster NLP with sequence-to-sequence networks and the Transformer architectureBuild applications for natural language translation and image captioningNVIDIA's invention of the GPU sparked the PC gaming market. The company's pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others.Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Preis: 43.86 € | Versand*: 0 €
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Was sind die Anwendungsmöglichkeiten von Natural Language Processing in der heutigen Technologie?
Natural Language Processing wird in der Spracherkennung für virtuelle Assistenten wie Siri oder Alexa verwendet. Es ermöglicht die automatische Übersetzung von Texten in verschiedene Sprachen. Zudem wird NLP in der Sentimentanalyse eingesetzt, um die Stimmung und Meinungen in sozialen Medien oder Kundenbewertungen zu analysieren.
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Was sind die grundlegenden Prinzipien und Anwendungen von Natural Language Processing (NLP)?
Die grundlegenden Prinzipien von Natural Language Processing beinhalten die Verarbeitung und Analyse von natürlicher Sprache durch Computer. Anwendungen von NLP umfassen Textübersetzung, Chatbots, Sentimentanalyse und Spracherkennung. NLP wird in verschiedenen Bereichen wie der Medizin, der Finanzbranche und der Kundenbetreuung eingesetzt.
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Was sind die aktuellen Anwendungen und zukünftigen Entwicklungen im Bereich Natural Language Processing?
Aktuelle Anwendungen von Natural Language Processing sind Chatbots, Spracherkennung und automatische Übersetzungssoftware. Zukünftige Entwicklungen könnten eine verbesserte semantische Analyse, personalisierte Sprachassistenten und eine bessere Integration von NLP in verschiedene Branchen umfassen. Es wird erwartet, dass NLP in Zukunft eine noch größere Rolle in der Automatisierung von Prozessen und der Verbesserung der Benutzererfahrung spielen wird.
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Was sind die wichtigsten Anwendungen von Natural Language Processing in der heutigen Technologie?
Die wichtigsten Anwendungen von Natural Language Processing in der heutigen Technologie sind Chatbots, automatische Übersetzungen und Sentiment-Analyse. NLP ermöglicht es Maschinen, menschliche Sprache zu verstehen und darauf zu reagieren, was die Interaktion mit Computern erleichtert. Durch NLP können Unternehmen effizienter kommunizieren, Kunden besser unterstützen und Daten analysieren.
Ähnliche Suchbegriffe für Natural Language Processing:
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Digital Video Processing
Over the years, thousands of engineering students and professionals relied on Digital Video Processing as the definitive, in-depth guide to digital image and video processing technology. Now, Dr. A. Murat Tekalp has completely revamped the first edition to reflect today’s technologies, techniques, algorithms, and trends. Digital Video Processing, Second Edition, reflects important advances in image processing, computer vision, and video compression, including new applications such as digital cinema, ultra-high-resolution video, and 3D video. This edition offers rigorous, comprehensive, balanced, and quantitative coverage of image filtering, motion estimation, tracking, segmentation, video filtering, and compression. Now organized and presented as a true tutorial, it contains updated problem sets and new MATLAB projects in every chapter. Coverage includes Multi-dimensional signals/systems: transforms, sampling, and lattice conversion Digital images and video: human vision, analog/digital video, and video quality Image filtering: gradient estimation, edge detection, scaling, multi-resolution representations, enhancement, de-noising, and restoration Motion estimation: image formation; motion models; differential, matching, optimization, and transform-domain methods; and 3D motion and shape estimation Video segmentation: color and motion segmentation, change detection, shot boundary detection, video matting, video tracking, and performance evaluation Multi-frame filtering: motion-compensated filtering, multi-frame standards conversion, multi-frame noise filtering, restoration, and super-resolution Image compression: lossless compression, JPEG, wavelets, and JPEG2000 Video compression: early standards, ITU-T H.264/MPEG-4 AVC, HEVC, Scalable Video Compression, and stereo/multi-view approaches
Preis: 97.36 € | Versand*: 0 € -
Body Language
Never mind what you think you’re saying, what is your body saying? Over half of our communication is through our bodies, but how many of us know how to decipher this non-verbal language? Body Language will make sure you get it right every time. In seven simple lessons you’ll become an expert at reading others and controlling your own gestures to get the response you want. This definitive and indispensable guide to body language will help you: Make a good impression and be instantly likeable Match what you’re saying to the signals you’re sending so you send out clear, credible messages Learn how to read other people’s faces, eyes and tone of voice effectively Decipher the language of the limbs, from folded arms to crossed legs Figure out quickly when someone is lying James Borg’s Body Language will give you the magic formula to mastering the power of body language – the ultimate way to achieve success in work and life. The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed.
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Digital Signal Processing
Modern coverage of the fundamentals, implementation and applications of digital signal processing techniques from a practical point of view.The past ten years has seen a significant growth in DSP applications throughout all areas of technology and this growth is expected well into the next millennium. This successful textbook covers most aspects of DSP found in undergraduate electrical, electronic or communications engineering courses. Unlike many other texts, it also covers a number of DSP techniques which are of particular relevance to industry such as adaptive filtering and multirate processing. The emphasis throughout the book is on the practical aspects of DSP.
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Discrete-Time Signal Processing
For senior/graduate-level courses in Discrete-Time Signal Processing. THE definitive, authoritative text on DSP — ideal for those with an introductory-level knowledge of signals and systems. Written by prominent DSP pioneers, it provides thorough treatment of the fundamental theorems and properties of discrete-time linear systems, filtering, sampling, and discrete-time Fourier Analysis. By focusing on the general and universal concepts in discrete-time signal processing, it remains vital and relevant to the new challenges arising in the field. The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed.
Preis: 53.49 € | Versand*: 0 €
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Wie beeinflusst Natural Language Processing die Entwicklung von Chatbots in der Kundenbetreuung und im Marketing?
Natural Language Processing ermöglicht es Chatbots, natürliche Sprache zu verstehen und darauf zu reagieren, was die Interaktion mit Kunden authentischer macht. Durch NLP können Chatbots Kundenanfragen besser verstehen und relevante Antworten liefern, was die Effizienz in der Kundenbetreuung erhöht. Zudem ermöglicht NLP eine personalisierte Kommunikation, da Chatbots die Sprache und Vorlieben der Kunden analysieren und entsprechend reagieren können. Im Marketing können Chatbots mithilfe von NLP personalisierte Empfehlungen und Angebote basierend auf den individuellen Bedürfnissen und Präferenzen der Kunden bereitstellen.
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Wie beeinflusst Natural Language Processing die Entwicklung von Chatbots in der Kundenbetreuung, medizinischen Diagnose und anderen Anwendungen?
Natural Language Processing (NLP) ermöglicht es Chatbots, natürliche Sprache zu verstehen und darauf zu reagieren, was die Interaktion mit Kunden in der Kundenbetreuung verbessert. In der medizinischen Diagnose kann NLP dazu beitragen, große Mengen an Patientendaten zu analysieren und relevante Informationen zu extrahieren, um Ärzten bei der Diagnose und Behandlung zu unterstützen. In anderen Anwendungen ermöglicht NLP eine präzisere Analyse von Textdaten, was zu einer verbesserten Automatisierung und Effizienz führt. Darüber hinaus kann NLP dazu beitragen, Sprachbarrieren zu überwinden und die Kommunikation in verschiedenen Sprachen zu erleichtern.
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Was sind die häufigsten Anwendungen von Natural Language Processing (NLP) in der heutigen Technologie und Wissenschaft?
Die häufigsten Anwendungen von NLP in der heutigen Technologie sind Chatbots für den Kundenservice, automatische Übersetzungsprogramme und Sentiment-Analyse in sozialen Medien. In der Wissenschaft wird NLP oft für die Analyse von großen Textmengen in Bereichen wie der Medizin, Linguistik und Psychologie eingesetzt. NLP hilft dabei, natürliche Sprache zu verstehen und zu verarbeiten, um menschenähnliche Interaktionen mit Computern zu ermöglichen.
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Wie beeinflusst Natural Language Processing die Entwicklung von Chatbots in der Kundenbetreuung, medizinischen Diagnose und automatisierten Übersetzungsprogrammen?
Natural Language Processing (NLP) ermöglicht es Chatbots, natürliche Sprache zu verstehen und darauf zu reagieren, was die Interaktion mit Kunden in der Kundenbetreuung verbessert. In der medizinischen Diagnose kann NLP dazu beitragen, große Mengen an medizinischen Daten zu analysieren und Muster zu erkennen, um präzisere Diagnosen zu stellen. Bei automatisierten Übersetzungsprogrammen ermöglicht NLP eine genauere und natürlichere Übersetzung von Texten in verschiedene Sprachen, was die Kommunikation über Sprachbarrieren hinweg erleichtert. Insgesamt trägt NLP dazu bei, die Effizienz und Genauigkeit von Chatbots in der Kundenbetreuung, medizinischen Diagnose und automatisierten Übersetzungsprogrammen zu verbessern.
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