Deep Learning Approach For Natural Language Processing Speech And Computer Vision

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Deep Learning Approach for Natural Language Processing, Speech, and Computer Vision

L. Ashok Kumar,D. Karthika Renuka

Author : L. Ashok Kumar,D. Karthika Renuka
Publisher : CRC Press
Page : 251 pages
File Size : 53,7 Mb
Release : 2023-05-22
Category : Business & Economics
ISBN : 9781000875607

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Deep Learning Approach for Natural Language Processing, Speech, and Computer Vision by L. Ashok Kumar,D. Karthika Renuka Pdf

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 Approach for Natural Language Processing, Speech, and Computer Vision

L. Ashok Kumar,D. Karthika Renukay

Author : L. Ashok Kumar,D. Karthika Renukay
Publisher : Unknown
Page : 0 pages
File Size : 44,9 Mb
Release : 2023
Category : Computer vision
ISBN : 1032391669

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Deep Learning Approach for Natural Language Processing, Speech, and Computer Vision by L. Ashok Kumar,D. Karthika Renukay Pdf

"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 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 for Natural Language Processing

Karthiek Reddy Bokka,Shubhangi Hora,Tanuj Jain,Monicah Wambugu

Author : Karthiek Reddy Bokka,Shubhangi Hora,Tanuj Jain,Monicah Wambugu
Publisher : Packt Publishing Ltd
Page : 372 pages
File Size : 41,7 Mb
Release : 2019-06-11
Category : Computers
ISBN : 9781838553678

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Deep Learning for Natural Language Processing by Karthiek Reddy Bokka,Shubhangi Hora,Tanuj Jain,Monicah Wambugu Pdf

Gain the knowledge of various deep neural network architectures and their application areas to conquer your NLP issues. Key FeaturesGain insights into the basic building blocks of natural language processingLearn how to select the best deep neural network to solve your NLP problemsExplore convolutional and recurrent neural networks and long short-term memory networksBook Description Applying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain. The book goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs. As you advance through this deep learning book, you’ll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In the later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search. By the end of this book, you will not only have sound knowledge of natural language processing but also be able to select the best text pre-processing and neural network models to solve a number of NLP issues. What you will learnUnderstand various pre-processing techniques for deep learning problemsBuild a vector representation of text using word2vec and GloVeCreate a named entity recognizer and parts-of-speech tagger with Apache OpenNLPBuild a machine translation model in KerasDevelop a text generation application using LSTMBuild a trigger word detection application using an attention modelWho this book is for If you’re an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the book for you. Strong working knowledge of Python, linear algebra, and machine learning is a must.

Deep Learning for NLP and Speech Recognition

Uday Kamath,John Liu,James Whitaker

Author : Uday Kamath,John Liu,James Whitaker
Publisher : Springer
Page : 621 pages
File Size : 46,5 Mb
Release : 2019-06-10
Category : Computers
ISBN : 9783030145965

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Deep Learning for NLP and Speech Recognition by Uday Kamath,John Liu,James Whitaker Pdf

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

Li Deng,Yang Liu

Author : Li Deng,Yang Liu
Publisher : Springer
Page : 329 pages
File Size : 55,5 Mb
Release : 2018-05-23
Category : Computers
ISBN : 9789811052095

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Deep Learning in Natural Language Processing by Li Deng,Yang Liu Pdf

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 Research Applications for Natural Language Processing

Ashok Kumar, L.,Karthika Renuka, Dhanaraj,Geetha, S.

Author : Ashok Kumar, L.,Karthika Renuka, Dhanaraj,Geetha, S.
Publisher : IGI Global
Page : 313 pages
File Size : 48,7 Mb
Release : 2022-12-09
Category : Computers
ISBN : 9781668460030

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Deep Learning Research Applications for Natural Language Processing by Ashok Kumar, L.,Karthika Renuka, Dhanaraj,Geetha, S. Pdf

Humans have the most advanced method of communication, which is known as natural language. While humans can use computers to send voice and text messages to each other, computers do not innately know how to process natural language. In recent years, deep learning has primarily transformed the perspectives of a variety of fields in artificial intelligence (AI), including speech, vision, and natural language processing (NLP). The extensive success of deep learning in a wide variety of applications has served as a benchmark for the many downstream tasks in AI. The field of computer vision has taken great leaps in recent years and surpassed humans in tasks related to detecting and labeling objects thanks to advances in deep learning and neural networks. Deep Learning Research Applications for Natural Language Processing explains the concepts and state-of-the-art research in the fields of NLP, speech, and computer vision. It provides insights into using the tools and libraries in Python for real-world applications. Covering topics such as deep learning algorithms, neural networks, and advanced prediction, this premier reference source is an excellent resource for computational linguists, software engineers, IT managers, computer scientists, students and faculty of higher education, libraries, researchers, and academicians.

Deep Learning: Theory, Architectures and Applications in Speech, Image and Language Processing

Gyanendra Verma,Rajesh Doriya

Author : Gyanendra Verma,Rajesh Doriya
Publisher : Bentham Science Publishers
Page : 270 pages
File Size : 44,9 Mb
Release : 2023-08-21
Category : Computers
ISBN : 9789815079227

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Deep Learning: Theory, Architectures and Applications in Speech, Image and Language Processing by Gyanendra Verma,Rajesh Doriya Pdf

This book is a detailed reference guide on deep learning and its applications. It aims to provide a basic understanding of deep learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by computer science academics and researchers. By the end of the book, the reader will become familiar with different deep learning approaches and models, and understand how to implement various deep learning algorithms using multiple frameworks and libraries. This book is divided into three parts. The first part explains the basic operating understanding, history, evolution, and challenges associated with deep learning. The basic concepts of mathematics and the hardware requirements for deep learning implementation, and some of its popular frameworks for medical applications are also covered. The second part is dedicated to sentiment analysis using deep learning and machine learning techniques. This book section covers the experimentation and application of deep learning techniques and architectures in real-world applications. It details the salient approaches, issues, and challenges in building ethically aligned machines. An approach inspired by traditional Eastern thought and wisdom is also presented. The final part covers artificial intelligence approaches used to explain the machine learning models that enhance transparency for the benefit of users. A review and detailed description of the use of knowledge graphs in generating explanations for black-box recommender systems and a review of ethical system design and a model for sustainable education is included in this section. An additional chapter demonstrates how a semi-supervised machine learning technique can be used for cryptocurrency portfolio management. The book is a timely reference for academicians, professionals, researchers and students at engineering and medical institutions working on artificial intelligence applications.

Machine Learning and Deep Learning in Natural Language Processing

Anitha S. Pillai,Roberto Tedesco

Author : Anitha S. Pillai,Roberto Tedesco
Publisher : CRC Press
Page : 234 pages
File Size : 53,5 Mb
Release : 2023-10-18
Category : Computers
ISBN : 9781000960891

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Machine Learning and Deep Learning in Natural Language Processing by Anitha S. Pillai,Roberto Tedesco Pdf

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.

Real-World Natural Language Processing

Masato Hagiwara

Author : Masato Hagiwara
Publisher : Simon and Schuster
Page : 334 pages
File Size : 41,5 Mb
Release : 2021-12-21
Category : Computers
ISBN : 9781638350392

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Real-World Natural Language Processing by Masato Hagiwara Pdf

Real-world Natural Language Processing shows you how to build the practical NLP applications that are transforming the way humans and computers work together. In Real-world Natural Language Processing you will learn how to: Design, develop, and deploy useful NLP applications Create named entity taggers Build machine translation systems Construct language generation systems and chatbots Use advanced NLP concepts such as attention and transfer learning 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, including chatbots, language detectors, and text classifiers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Training computers to interpret and generate speech and text is a monumental challenge, and the payoff for reducing labor and improving human/computer interaction is huge! Th e field of Natural Language Processing (NLP) is advancing rapidly, with countless new tools and practices. This unique book offers an innovative collection of NLP techniques with applications in machine translation, voice assistants, text generation, and more. About the book Real-world Natural Language Processing shows you how to build the practical NLP applications that are transforming the way humans and computers work together. Guided by clear explanations of each core NLP topic, you’ll create many interesting applications including a sentiment analyzer and a chatbot. Along the way, you’ll use Python and open source libraries like AllenNLP and HuggingFace Transformers to speed up your development process. What's inside Design, develop, and deploy useful NLP applications Create named entity taggers Build machine translation systems Construct language generation systems and chatbots About the reader For Python programmers. No prior machine learning knowledge assumed. About the author Masato Hagiwara received his computer science PhD from Nagoya University in 2009. He has interned at Google and Microsoft Research, and worked at Duolingo as a Senior Machine Learning Engineer. He now runs his own research and consulting company. Table of Contents PART 1 BASICS 1 Introduction to natural language processing 2 Your first NLP application 3 Word and document embeddings 4 Sentence classification 5 Sequential labeling and language modeling PART 2 ADVANCED MODELS 6 Sequence-to-sequence models 7 Convolutional neural networks 8 Attention and Transformer 9 Transfer learning with pretrained language models PART 3 PUTTING INTO PRODUCTION 10 Best practices in developing NLP applications 11 Deploying and serving NLP applications

Deep Learning for Natural Language Processing

Palash Goyal,Sumit Pandey,Karan Jain

Author : Palash Goyal,Sumit Pandey,Karan Jain
Publisher : Apress
Page : 290 pages
File Size : 55,9 Mb
Release : 2018-06-26
Category : Computers
ISBN : 9781484236857

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Deep Learning for Natural Language Processing by Palash Goyal,Sumit Pandey,Karan Jain Pdf

Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system. This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways. What You Will Learn Gain the fundamentals of deep learning and its mathematical prerequisites Discover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classification Who This Book Is For Software developers who are curious to try out deep learning with NLP.

Artificial Vision and Language Processing for Robotics

Álvaro Morena Alberola,Gonzalo Molina Gallego,Unai Garay Maestre

Author : Álvaro Morena Alberola,Gonzalo Molina Gallego,Unai Garay Maestre
Publisher : Packt Publishing Ltd
Page : 356 pages
File Size : 40,8 Mb
Release : 2019-04-30
Category : Computers
ISBN : 9781838557669

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Artificial Vision and Language Processing for Robotics by Álvaro Morena Alberola,Gonzalo Molina Gallego,Unai Garay Maestre Pdf

Create end-to-end systems that can power robots with artificial vision and deep learning techniques Key FeaturesStudy ROS, the main development framework for robotics, in detailLearn all about convolutional neural networks, recurrent neural networks, and roboticsCreate a chatbot to interact with the robotBook Description Artificial Vision and Language Processing for Robotics begins by discussing the theory behind robots. You'll compare different methods used to work with robots and explore computer vision, its algorithms, and limits. You'll then learn how to control the robot with natural language processing commands. You'll study Word2Vec and GloVe embedding techniques, non-numeric data, recurrent neural network (RNNs), and their advanced models. You'll create a simple Word2Vec model with Keras, as well as build a convolutional neural network (CNN) and improve it with data augmentation and transfer learning. You'll study the ROS and build a conversational agent to manage your robot. You'll also integrate your agent with the ROS and convert an image to text and text to speech. You'll learn to build an object recognition system using a video. By the end of this book, you'll have the skills you need to build a functional application that can integrate with a ROS to extract useful information about your environment. What you will learnExplore the ROS and build a basic robotic systemUnderstand the architecture of neural networksIdentify conversation intents with NLP techniquesLearn and use the embedding with Word2Vec and GloVeBuild a basic CNN and improve it using generative modelsUse deep learning to implement artificial intelligence(AI)and object recognitionDevelop a simple object recognition system using CNNsIntegrate AI with ROS to enable your robot to recognize objectsWho this book is for Artificial Vision and Language Processing for Robotics is for robotics engineers who want to learn how to integrate computer vision and deep learning techniques to create complete robotic systems. It will prove beneficial to you if you have working knowledge of Python and a background in deep learning. Knowledge of the ROS is a plus.

Neural Networks for Natural Language Processing

S., Sumathi,M., Janani

Author : S., Sumathi,M., Janani
Publisher : IGI Global
Page : 227 pages
File Size : 53,6 Mb
Release : 2019-11-29
Category : Computers
ISBN : 9781799811619

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Neural Networks for Natural Language Processing by S., Sumathi,M., Janani Pdf

Information in today’s advancing world is rapidly expanding and becoming widely available. This eruption of data has made handling it a daunting and time-consuming task. Natural language processing (NLP) is a method that applies linguistics and algorithms to large amounts of this data to make it more valuable. NLP improves the interaction between humans and computers, yet there remains a lack of research that focuses on the practical implementations of this trending approach. Neural Networks for Natural Language Processing is a collection of innovative research on the methods and applications of linguistic information processing and its computational properties. This publication will support readers with performing sentence classification and language generation using neural networks, apply deep learning models to solve machine translation and conversation problems, and apply deep structured semantic models on information retrieval and natural language applications. While highlighting topics including deep learning, query entity recognition, and information retrieval, this book is ideally designed for research and development professionals, IT specialists, industrialists, technology developers, data analysts, data scientists, academics, researchers, and students seeking current research on the fundamental concepts and techniques of natural language processing.

Deep Learning for Natural Language Processing

Jason Brownlee

Author : Jason Brownlee
Publisher : Machine Learning Mastery
Page : 413 pages
File Size : 51,9 Mb
Release : 2017-11-21
Category : Computers
ISBN : 8210379456XXX

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Deep Learning for Natural Language Processing by Jason Brownlee Pdf

Deep learning methods are achieving state-of-the-art results on challenging machine learning problems such as describing photos and translating text from one language to another. In this new laser-focused Ebook, finally cut through the math, research papers and patchwork descriptions about natural language processing. Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what natural language processing is, the promise of deep learning in the field, how to clean and prepare text data for modeling, and how to develop deep learning models for your own natural language processing projects.

Deep Learning Approaches for Spoken and Natural Language Processing

Virender Kadyan,Amitoj Singh,Mohit Mittal,Laith Abualigah

Author : Virender Kadyan,Amitoj Singh,Mohit Mittal,Laith Abualigah
Publisher : Springer Nature
Page : 171 pages
File Size : 43,6 Mb
Release : 2022-01-01
Category : Technology & Engineering
ISBN : 9783030797782

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Deep Learning Approaches for Spoken and Natural Language Processing by Virender Kadyan,Amitoj Singh,Mohit Mittal,Laith Abualigah Pdf

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.

Natural Language Processing with PyTorch

Delip Rao,Brian McMahan

Author : Delip Rao,Brian McMahan
Publisher : O'Reilly Media
Page : 256 pages
File Size : 50,8 Mb
Release : 2019-01-22
Category : Computers
ISBN : 9781491978207

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Natural Language Processing with PyTorch by Delip Rao,Brian McMahan Pdf

Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library. Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations. Explore computational graphs and the supervised learning paradigm Master the basics of the PyTorch optimized tensor manipulation library Get an overview of traditional NLP concepts and methods Learn the basic ideas involved in building neural networks Use embeddings to represent words, sentences, documents, and other features Explore sequence prediction and generate sequence-to-sequence models Learn design patterns for building production NLP systems

Deep Learning Approach For Natural Language Processing Speech And Computer Vision

Deep Learning Approach for Natural Language Processing, Speech, and Computer Vision by L. Ashok Kumar,D. Karthika Renuka Pdf

Deep Learning Approach for Natural Language Processing, Speech, and Computer Vision by L. Ashok Kumar,D. Karthika Renukay Pdf

Deep Learning for Natural Language Processing by Karthiek Reddy Bokka,Shubhangi Hora,Tanuj Jain,Monicah Wambugu Pdf

Deep Learning for NLP and Speech Recognition by Uday Kamath,John Liu,James Whitaker Pdf

Deep Learning in Natural Language Processing by Li Deng,Yang Liu Pdf

Deep Learning Research Applications for Natural Language Processing by Ashok Kumar, L.,Karthika Renuka, Dhanaraj,Geetha, S. Pdf

Deep Learning: Theory, Architectures and Applications in Speech, Image and Language Processing by Gyanendra Verma,Rajesh Doriya Pdf

Machine Learning and Deep Learning in Natural Language Processing by Anitha S. Pillai,Roberto Tedesco Pdf

Real-World Natural Language Processing by Masato Hagiwara Pdf

Deep Learning for Natural Language Processing by Palash Goyal,Sumit Pandey,Karan Jain Pdf

Artificial Vision and Language Processing for Robotics by Álvaro Morena Alberola,Gonzalo Molina Gallego,Unai Garay Maestre Pdf

Neural Networks for Natural Language Processing by S., Sumathi,M., Janani Pdf

Deep Learning for Natural Language Processing by Jason Brownlee Pdf

Deep Learning Approaches for Spoken and Natural Language Processing by Virender Kadyan,Amitoj Singh,Mohit Mittal,Laith Abualigah Pdf

Natural Language Processing with PyTorch by Delip Rao,Brian McMahan Pdf

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