Deep Structured Models For Text Understanding
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Deep Structured Models For Text Understanding
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Author : Yifan Su
language : en
Publisher:
Release Date : 2016
Deep Structured Models For Text Understanding written by Yifan Su and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Deep Learning With Structured Data
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Author : Mark Ryan
language : en
Publisher: Simon and Schuster
Release Date : 2020-12-08
Deep Learning With Structured Data written by Mark Ryan and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-08 with Computers categories.
Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Summary Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world businesses depend on. Filled with practical, relevant applications, this book teaches you how deep learning can augment your existing machine learning and business intelligence systems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Here’s a dirty secret: Half of the time in most data science projects is spent cleaning and preparing data. But there’s a better way: Deep learning techniques optimized for tabular data and relational databases deliver insights and analysis without requiring intense feature engineering. Learn the skills to unlock deep learning performance with much less data filtering, validating, and scrubbing. About the book Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Get started using a dataset based on the Toronto transit system. As you work through the book, you’ll learn how easy it is to set up tabular data for deep learning, while solving crucial production concerns like deployment and performance monitoring. What's inside When and where to use deep learning The architecture of a Keras deep learning model Training, deploying, and maintaining models Measuring performance About the reader For readers with intermediate Python and machine learning skills. About the author Mark Ryan is a Data Science Manager at Intact Insurance. He holds a Master's degree in Computer Science from the University of Toronto. Table of Contents 1 Why deep learning with structured data? 2 Introduction to the example problem and Pandas dataframes 3 Preparing the data, part 1: Exploring and cleansing the data 4 Preparing the data, part 2: Transforming the data 5 Preparing and building the model 6 Training the model and running experiments 7 More experiments with the trained model 8 Deploying the model 9 Recommended next steps
Text Analytics With Python
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Author : Dipanjan Sarkar
language : en
Publisher: Apress
Release Date : 2019-05-21
Text Analytics With Python written by Dipanjan Sarkar and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-21 with Computers categories.
Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. This second edition has gone through a major revamp and introduces several significant changes and new topics based on the recent trends in NLP. You’ll see how to use the latest state-of-the-art frameworks in NLP, coupled with machine learning and deep learning models for supervised sentiment analysis powered by Python to solve actual case studies. Start by reviewing Python for NLP fundamentals on strings and text data and move on to engineering representation methods for text data, including both traditional statistical models and newer deep learning-based embedding models. Improved techniques and new methods around parsing and processing text are discussed as well. Text summarization and topic models have been overhauled so the book showcases how to build, tune, and interpret topic models in the context of an interest dataset on NIPS conference papers. Additionally, the book covers text similarity techniques with a real-world example of movie recommenders, along with sentiment analysis using supervised and unsupervised techniques. There is also a chapter dedicated to semantic analysis where you’ll see how to build your own named entity recognition (NER) system from scratch. While the overall structure of the book remains the same, the entire code base, modules, and chapters has been updated to the latest Python 3.x release. What You'll Learn • Understand NLP and text syntax, semantics and structure• Discover text cleaning and feature engineering• Review text classification and text clustering • Assess text summarization and topic models• Study deep learning for NLP Who This Book Is For IT professionals, data analysts, developers, linguistic experts, data scientists and engineers and basically anyone with a keen interest in linguistics, analytics and generating insights from textual data.
Models Of Understanding Text
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Author : Bruce K. Britton
language : en
Publisher: Psychology Press
Release Date : 2014-02-25
Models Of Understanding Text written by Bruce K. Britton and has been published by Psychology Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-02-25 with Psychology categories.
What is text understanding? It is the dynamic process of constructing coherent representations and inferences at multiple levels of text and context, within the bottleneck of a limited-capacity working memory. The field of text and discourse has advanced to the point where researchers have developed sophisticated models of comprehension, and identified the particular assumptions that underlie comprehension mechanisms in precise analytical or mathematical detail. The models offer a priori predictions about thought and behavior, not merely ad hoc descriptions of data. Indeed, the field has evolved to a mature science. The contributors to this volume collectively cover the major models of comprehension in the field of text and discourse. Other books are either narrow -- covering only a single theoretical framework -- or do not focus on systematic modeling efforts. In addition, this book focuses on deep levels of understanding rather than language codes, syntax, and other shallower levels of text analysis. As such, it provides readers with up-to-date information on current psychological models specified in quantitative or analytical detail.
Natural Language Processing
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Author : Yue Zhang
language : en
Publisher: Cambridge University Press
Release Date : 2021-01-07
Natural Language Processing written by Yue Zhang and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-07 with Computers categories.
This undergraduate textbook introduces essential machine learning concepts in NLP in a unified and gentle mathematical framework.
The Universal Deep Structure Of Modern Poetry
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Author : John A.F. Hopkins
language : en
Publisher: Cambridge Scholars Publishing
Release Date : 2020-04-02
The Universal Deep Structure Of Modern Poetry written by John A.F. Hopkins and has been published by Cambridge Scholars Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-02 with Poetry categories.
With something of a poetry renaissance currently under way worldwide, there is now, more than ever, a need for a solidly-based methodology for interpreting poems: something more empirical than traditional âlit-critâ approaches, and something more linguistically-informed than the version of âpostmodernismâ rampant in certain Anglophone universities. The latter approach, which tends to allow the individual reader to do what he/she likes with a poetic text, is inadequate to interpret modernist poetry, whose English-language precursors may be found in the late Romantics; its pioneers were already writing (in France) as early as 1840. What is so different about the modernists? Most importantly, their works are monumental, in that they are strongly resistant to deconstruction. Contributing to this resistance is the fact that they are built around two deep-level propositions, each of which generates a set of indirectly-signifying images, sharing the same internal structure, but having a different vocabulary. Thus, they do not signify according to linear narrative, but according to these propositionsâand the relation between themâwhich may be reconstructed by a careful comparison of images on the textual surface. Every textâas subject-signârefers to an intertextual object-sign, which is usually another poem, but may also be a film or other form of art. Mediating between these two signs is their reader-constructed interpretant, which completes the semiotic triad. As this book shows, the novelty of this sign is thrown into relief by the contrast it makes with a lexical counterpart from the readerâs experience, which differs from the interpretant in structure. The bookâs inclusion of French and Japanese, as well as English poems, shows that deep-level signifying mechanisms may well be universal, with considerable research and pedagogical implications.
Machine Reading Comprehension
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Author : Chenguang Zhu
language : en
Publisher: Elsevier
Release Date : 2021-03-20
Machine Reading Comprehension written by Chenguang Zhu and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-20 with Computers categories.
Machine reading comprehension (MRC) is a cutting-edge technology in natural language processing (NLP). MRC has recently advanced significantly, surpassing human parity in several public datasets. It has also been widely deployed by industry in search engine and quality assurance systems. Machine Reading Comprehension: Algorithms and Practice performs a deep-dive into MRC, offering a resource on the complex tasks this technology involves. The title presents the fundamentals of NLP and deep learning, before introducing the task, models, and applications of MRC. This volume gives theoretical treatment to solutions and gives detailed analysis of code, and considers applications in real-world industry. The book includes basic concepts, tasks, datasets, NLP tools, deep learning models and architecture, and insight from hands-on experience. In addition, the title presents the latest advances from the past two years of research. Structured into three sections and eight chapters, this book presents the basis of MRC; MRC models; and hands-on issues in application. This book offers a comprehensive solution for researchers in industry and academia who are looking to understand and deploy machine reading comprehension within natural language processing. Presents the first comprehensive resource on machine reading comprehension (MRC) Performs a deep-dive into MRC, from fundamentals to latest developments Offers the latest thinking and research in the field of MRC, including the BERT model Provides theoretical discussion, code analysis, and real-world applications of MRC Gives insight from research which has led to surpassing human parity in MRC
Software Design Cognitive Aspect
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Author : Francoise Detienne
language : en
Publisher: Springer Science & Business Media
Release Date : 2001-10-12
Software Design Cognitive Aspect written by Francoise Detienne and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-10-12 with Computers categories.
Covering a variety of areas including software analysis, design, coding and maintenance, this text details the research conducted since the 1970s in this fast-developing field before going on to define a computer program from the viewpoint of computing and cognitive psychology. The two essential sides of programming, software production and software understanding, are given detailed treatment, with parallels drawn throughout between studies on processing texts written in natural language and processing computer programs. Of particular interest to researchers, practitioners and graduates in cognitive psychology, cognitive ergonomics and computer science.
Methods Of Text And Discourse Analysis
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Author : Stefan Titscher
language : en
Publisher: SAGE
Release Date : 2000-07-28
Methods Of Text And Discourse Analysis written by Stefan Titscher and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-07-28 with Language Arts & Disciplines categories.
'This volume is the most comprehensive overview to date of sociologically orientated approaches to text and discourse analysis and is worth reading even for those who are interested only in purely linguistiv approaches to text and discourse. Its main merit, I think, is that it intorduces approaches which up to now have hardley been admitted into the universe of scientific discourse' - Discourse Studies Methods of Text and Discourse Analysis provides the most comprehensive overview currently available of linguistic and sociological approaches to text and discourse analysis. Among the 10 linguistic and sociological models surveyed in this book some of the more important are Grounded Theory, Content Analysis, Conversation Analysis and Critical Discourse Analysis. The book presents each approach according to a standardised format, which allows for direct systematic comparisons. The fully annotated lists of sources provide readers with an additional means of evaluation of the competing analytical methods. Interdisciplinary and international in its aims, Methods of Text and Discourse Analysis suggests the benefits both linguists and sociologists will derive from a more intimate knowledge of each others' methods and procedures.
Deep Learning
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Author : Ian Goodfellow
language : en
Publisher: MIT Press
Release Date : 2016-11-10
Deep Learning written by Ian Goodfellow and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-10 with Computers categories.
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.