Produkte zum Begriff NLTK:
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U&P AI - Natural Language Processing (NLP) with Python Alpha Academy Code
The U&P AI - Natural Language Processing (NLP) with Python course is a comprehensive guide to mastering NLP techniques using Python programming. Key Features: Learn the fundamentals of Natural Language Processing (NLP) with Python Dive into advanced NLP techniques such as sentiment analysis and text classification Gain practical experience through hands-on coding exercises and projects Benefits: Empowers learners to analyse and derive insights from textual data Enhances programming skills wit...
Preis: 10.99 € | Versand*: 0.00 EUR € -
U&P AI - Natural Language Processing (NLP) with Python Alpha Academy Code
Der Kurs „U&P AI – Natural Language Processing (NLP) mit Python" ist ein umfassender Leitfaden zur Erlernung von NLP-Techniken mithilfe der Python-Programmierung. Hauptmerkmale: Lernen Sie die Grundlagen der Verarbeitung natürlicher Sprache (NLP) mit Python Tauchen Sie ein in fortgeschrittene NLP-Techniken wie Stimmungsanalyse und Textklassifizierung Sammeln Sie praktische Erfahrungen durch praxisnahe Programmierübungen und Projekte Vorteile: Befähigt Lernende, Textdaten zu analysieren und da...
Preis: 10.99 € | Versand*: 0.00 EUR € -
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: 66.33 € | Versand*: 0 €
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Wie kann man sich selbst Machine Learning, Künstliche Intelligenz und Natural Language Processing beibringen?
Um sich selbst Machine Learning, Künstliche Intelligenz und Natural Language Processing beizubringen, gibt es verschiedene Möglichkeiten. Man kann Online-Kurse und Tutorials nutzen, um die Grundlagen zu erlernen und praktische Erfahrungen zu sammeln. Es ist auch hilfreich, an Projekten zu arbeiten und mit vorhandenen Tools und Bibliotheken zu experimentieren. Zudem kann der Austausch mit anderen Fachleuten in Foren und Communitys dabei helfen, Fragen zu klären und neue Ideen zu entwickeln.
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Wie nutzt man Windows Speech Recognition richtig in Visual Basic?
Um Windows Speech Recognition in Visual Basic zu nutzen, müssen Sie zunächst sicherstellen, dass Sie die entsprechenden Verweise auf die Speech API in Ihrem Projekt hinzugefügt haben. Dann können Sie die SpeechRecognitionEngine-Klasse verwenden, um Spracheingaben zu erkennen und zu verarbeiten. Sie können Ereignisse wie SpeechRecognized und SpeechRecognitionRejected abonnieren, um auf erkannte oder abgelehnte Spracheingaben zu reagieren. Schließlich können Sie die RecognizeAsync-Methode aufrufen, um die Spracherkennung zu starten und auf Benutzereingaben zu warten.
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Was ist maschinelles Lernen?
Maschinelles Lernen ist ein Teilgebiet der künstlichen Intelligenz, bei dem Computer Algorithmen entwickeln, die aus Daten lernen und Muster erkennen können. Dabei werden Modelle erstellt, die es Computern ermöglichen, eigenständig Probleme zu lösen, ohne explizit programmiert zu werden. Maschinelles Lernen wird in verschiedenen Bereichen eingesetzt, wie zum Beispiel in der Bilderkennung, Spracherkennung, medizinischen Diagnosen oder auch im Bereich des autonomen Fahrens. Es ermöglicht es Computern, aus Erfahrungen zu lernen und sich kontinuierlich zu verbessern.
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Wie kann man Processing lernen?
Um Processing zu lernen, gibt es verschiedene Möglichkeiten. Eine Möglichkeit ist es, Tutorials und Online-Kurse zu nutzen, die speziell für Anfänger entwickelt wurden. Es gibt auch Bücher und Videos, die Schritt für Schritt Anleitungen und Beispiele bieten. Eine weitere Möglichkeit ist es, an Workshops oder Kursen teilzunehmen, in denen man von erfahrenen Processing-Entwicklern lernen kann. Es ist auch hilfreich, mit anderen Entwicklern in der Community zu interagieren und von ihren Erfahrungen zu lernen.
Ähnliche Suchbegriffe für NLTK:
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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 € -
Fischer, Jörn: Maschinelles Lernen für Dummies
Maschinelles Lernen für Dummies , Maschinelles Lernen ist eines der wichtigsten Teilgebiete der künstlichen Intelligenz und das Verstehen und Entwickeln von passenden Algorithmen bleibt die große Herausforderung. Dieses Buch bietet einen außergewöhnlich umfassenden Überblick über die neuesten Algorithmen und die bereits bewährten Verfahren. Jörn Fischer beschreibt nicht nur deren Funktionsweise, sondern gibt für alle Bereiche verständliche Beispiele, die detailliert beschrieben und leicht nachvollziehbar sind. Außerdem werden hilfreiche Methoden zur Fehlersuche und -beseitigung an die Hand gegeben. , Studium & Erwachsenenbildung > Fachbücher, Lernen & Nachschlagen
Preis: 28.00 € | 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 € -
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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Wie wird maschinelles Lernen in der Sprachverarbeitung eingesetzt, um die menschliche Sprache zu verstehen und zu verarbeiten?
Maschinelles Lernen wird in der Sprachverarbeitung eingesetzt, um Algorithmen zu trainieren, die natürliche Sprache verstehen und verarbeiten können. Durch die Verwendung von großen Datensätzen und neuronalen Netzwerken können Maschinen Muster erkennen und Sprache übersetzen oder generieren. Dies ermöglicht Anwendungen wie Spracherkennung, Übersetzung und Chatbots.
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Wie kann man Natural Horsemanship lernen?
Um Natural Horsemanship zu lernen, ist es empfehlenswert, Kurse oder Workshops bei erfahrenen Trainern zu besuchen. Dort kann man die Grundlagen dieser Methode erlernen und praktische Erfahrungen sammeln. Zudem kann man Bücher, Videos und Online-Ressourcen nutzen, um sein Wissen zu vertiefen. Es ist auch wichtig, regelmäßig mit Pferden zu arbeiten und sich Zeit zu nehmen, um ihr Verhalten zu beobachten und zu verstehen. Letztendlich ist es wichtig, geduldig und einfühlsam im Umgang mit den Pferden zu sein, um eine harmonische Beziehung aufzubauen.
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Ist maschinelles Lernen nur ein Hype?
Nein, maschinelles Lernen ist kein Hype. Es handelt sich um eine Technologie, die es Computern ermöglicht, aus Daten zu lernen und Vorhersagen oder Entscheidungen zu treffen. Es hat bereits viele Anwendungen in verschiedenen Bereichen wie Medizin, Finanzen und Automobilindustrie gefunden und wird voraussichtlich weiterhin an Bedeutung gewinnen.
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Lohnt es sich, "The C Programming Language" zu lernen?
Ja, es lohnt sich, "The C Programming Language" zu lernen, da C eine weit verbreitete und mächtige Programmiersprache ist. Sie wird in vielen Bereichen der Softwareentwicklung eingesetzt und bietet eine solide Grundlage für das Verständnis von Programmierung und Algorithmen. Das Buch selbst gilt als ein Klassiker und bietet eine umfassende Einführung in die Sprache.
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