Deep Learning For Nlp And Speech Recognition
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Deep Learning For Nlp And Speech Recognition
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Author : Uday Kamath
language : en
Publisher: Springer
Release Date : 2019-06-10
Deep Learning For Nlp And Speech Recognition written by Uday Kamath and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-10 with Computers categories.
This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.
Deep Learning For Nlp And Speech Recognition
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Author : Uday Kamath
language : en
Publisher: Springer
Release Date : 2020-07-08
Deep Learning For Nlp And Speech Recognition written by Uday Kamath and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-08 with Computers categories.
This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.
Deep Learning In Natural Language Processing
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Author : Li Deng
language : en
Publisher: Springer
Release Date : 2018-05-23
Deep Learning In Natural Language Processing written by Li Deng and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-23 with Computers categories.
In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including speech, vision, natural language, robotics, and game playing. In particular, the striking success of deep learning in a wide variety of natural language processing (NLP) applications has served as a benchmark for the advances in one of the most important tasks in artificial intelligence. This book reviews the state of the art of deep learning research and its successful applications to major NLP tasks, including speech recognition and understanding, dialogue systems, lexical analysis, parsing, knowledge graphs, machine translation, question answering, sentiment analysis, social computing, and natural language generation from images. Outlining and analyzing various research frontiers of NLP in the deep learning era, it features self-contained, comprehensive chapters written by leading researchers in the field. A glossary of technical terms and commonly used acronyms in the intersection of deep learning and NLP is also provided. The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers and industrial researchers, as well as anyone interested in deep learning and natural language processing.
Deep Learning Approaches For Spoken And Natural Language Processing
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Author : Virender Kadyan
language : en
Publisher: Springer Nature
Release Date : 2022-01-01
Deep Learning Approaches For Spoken And Natural Language Processing written by Virender Kadyan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-01 with Technology & Engineering categories.
This book provides insights into how deep learning techniques impact language and speech processing applications. The authors discuss the promise, limits and the new challenges in deep learning. The book covers the major differences between the various applications of deep learning and the classical machine learning techniques. The main objective of the book is to present a comprehensive survey of the major applications and research oriented articles based on deep learning techniques that are focused on natural language and speech signal processing. The book is relevant to academicians, research scholars, industrial experts, scientists and post graduate students working in the field of speech signal and natural language processing and would like to add deep learning to enhance capabilities of their work. Discusses current research challenges and future perspective about how deep learning techniques can be applied to improve NLP and speech processing applications; Presents and escalates the research trends and future direction of language and speech processing; Includes theoretical research, experimental results, and applications of deep learning.
Deep Learning Approach For Natural Language Processing Speech And Computer Vision
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Author : L. Ashok Kumar
language : en
Publisher: CRC Press
Release Date : 2023-05-22
Deep Learning Approach For Natural Language Processing Speech And Computer Vision written by L. Ashok Kumar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-22 with Business & Economics categories.
Deep Learning Approach for Natural Language Processing, Speech, and Computer Vision provides an overview of general deep learning methodology and its applications of natural language processing (NLP), speech, and computer vision tasks. It simplifies and presents the concepts of deep learning in a comprehensive manner, with suitable, full-fledged examples of deep learning models, with an aim to bridge the gap between the theoretical and the applications using case studies with code, experiments, and supporting analysis. Features: Covers latest developments in deep learning techniques as applied to audio analysis, computer vision, and natural language processing. Introduces contemporary applications of deep learning techniques as applied to audio, textual, and visual processing. Discovers deep learning frameworks and libraries for NLP, speech, and computer vision in Python. Gives insights into using the tools and libraries in Python for real-world applications. Provides easily accessible tutorials and real-world case studies with code to provide hands-on experience. This book is aimed at researchers and graduate students in computer engineering, image, speech, and text processing.
Deep Learning In Natural Language Processing
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Author : Li Deng
language : en
Publisher:
Release Date : 2018
Deep Learning In Natural Language Processing written by Li Deng and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Artificial Intelligence (incl. Robotics) categories.
In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including speech, vision, natural language, robotics, and game playing. In particular, the striking success of deep learning in a wide variety of natural language processing (NLP) applications has served as a benchmark for the advances in one of the most important tasks in artificial intelligence. This book reviews the state of the art of deep learning research and its successful applications to major NLP tasks, including speech recognition and understanding, dialogue systems, lexical analysis, parsing, knowledge graphs, machine translation, question answering, sentiment analysis, social computing, and natural language generation from images. Outlining and analyzing various research frontiers of NLP in the deep learning era, it features self-contained, comprehensive chapters written by leading researchers in the field. A glossary of technical terms and commonly used acronyms in the intersection of deep learning and NLP is also provided. The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers and industrial researchers, as well as anyone interested in deep learning and natural language processing.
Machine Learning And Deep Learning In Natural Language Processing
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Author : Anitha S. Pillai
language : en
Publisher: CRC Press
Release Date : 2023-10-18
Machine Learning And Deep Learning In Natural Language Processing written by Anitha S. Pillai and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-18 with Computers categories.
Natural Language Processing (NLP) is a sub-field of Artificial Intelligence, linguistics, and computer science and is concerned with the generation, recognition, and understanding of human languages, both written and spoken. NLP systems examine the grammatical structure of sentences as well as the specific meanings of words, and then they utilize algorithms to extract meaning and produce results. Machine Learning and Deep Learning in Natural Language Processing aims at providing a review of current Neural Network techniques in the NLP field, in particular about Conversational Agents (chatbots), Text-to-Speech, management of non-literal content – like emotions, but also satirical expressions – and applications in the healthcare field. NLP has the potential to be a disruptive technology in various healthcare fields, but so far little attention has been devoted to that goal. This book aims at providing some examples of NLP techniques that can, for example, restore speech, detect Parkinson’s disease, or help psychotherapists. This book is intended for a wide audience. Beginners will find useful chapters providing a general introduction to NLP techniques, while experienced professionals will appreciate the chapters about advanced management of emotion, empathy, and non-literal content.
New Era For Robust Speech Recognition
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Author : Shinji Watanabe
language : en
Publisher: Springer
Release Date : 2017-10-30
New Era For Robust Speech Recognition written by Shinji Watanabe and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-30 with Computers categories.
This book covers the state-of-the-art in deep neural-network-based methods for noise robustness in distant speech recognition applications. It provides insights and detailed descriptions of some of the new concepts and key technologies in the field, including novel architectures for speech enhancement, microphone arrays, robust features, acoustic model adaptation, training data augmentation, and training criteria. The contributed chapters also include descriptions of real-world applications, benchmark tools and datasets widely used in the field. This book is intended for researchers and practitioners working in the field of speech processing and recognition who are interested in the latest deep learning techniques for noise robustness. It will also be of interest to graduate students in electrical engineering or computer science, who will find it a useful guide to this field of research.
Deep Learning For Nlp Using Python
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Author : Tyler Edwards
language : en
Publisher:
Release Date : 2018
Deep Learning For Nlp Using Python written by Tyler Edwards and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
"In this course, you'll expand your NLP knowledge and skills while implementing deep learning tools to perform complex tasks. You'll start by preparing your environment for NLP and then quickly learn about language structure and how we can break sentences down to extract information and uncover the underlying meaning. After reviewing the basics, we'll move on to speech recognition and show how deep learning can be used to build speech recognition applications. In order to give you the best hands-on experience, the course includes a wide variety of practical real world examples. You'll discover how a Naive Bayes algorithm can be used for Binary and Multiclass text classification. We'll show you how a binary classifier can be used to determine if a product review would best be classified as positive or negative. You'll also learn how document classifiers can be used to predict information about the author of a text like their age, gender, or where they're from. Finally speech recognition systems will be introduced and you'll learn how to apply deep learning techniques to build your own speech to text application. We'll walk through two examples, step-by-step, showing how to build and train neural networks to understand spoken audio inputs. By the end of this tutorial, you'll have a better understanding of NLP and will have worked on multiple examples that implement deep learning to solve real-world spoken language problems. In particular, you'll be able to discover useful information and extract key insights from piles of natural language data."--Resource description page.
Python Based Machine Learning And Deep Learning For Natural Language Processing
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Author : Nitin Dixit
language : en
Publisher: Xoffencer international book publication house
Release Date : 2023-02-22
Python Based Machine Learning And Deep Learning For Natural Language Processing written by Nitin Dixit and has been published by Xoffencer international book publication house this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-22 with Computers categories.
NLP is an interdisciplinary topic that integrates computer science, artificial intelligence, and linguistics to create algorithms and models that can process and interpret human language. The purpose of natural language processing (NLP) is to allow computers to comprehend, interpret, and produce human language, which includes speech and text. Chatbots for customer service, sentiment analysis for marketing and social media, named entity recognition for information extraction, machine translation for multilingual communication, and speech recognition for handsfree contact with technology are just a few examples. Advances in machine learning, deep learning, and big data have fueled the development of NLP approaches, which continue to improve to meet the demands of new applications. Python is one of the most popular programming languages for natural language processing (NLP) because of its ease of use, readability, and the availability of strong libraries and tools such as NLTK, spaCy, and Gensim. 1.1 OVERVIEW Natural Language Processing (NLP) is a branch of computer science, artificial intelligence, and computational linguistics dealing with computer-human interaction. NLP's purpose is to enable computers to analyse, comprehend, and produce human language, which includes speech and text. This has resulted in a wide range of applications in various industries, including customer service chatbots, sentiment analysis for marketing and social media, named entity recognition for information extraction, machine translation for multilingual communication, and speech recognition for hands-free technology interaction