The Future Of Ai Machine Learning Deep Learning And Natural Language Processing
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The Future Of Ai Machine Learning Deep Learning And Natural Language Processing
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Author : Dr.Konda Hari Krishna
language : en
Publisher: Leilani Katie Publication
Release Date : 2025-04-03
The Future Of Ai Machine Learning Deep Learning And Natural Language Processing written by Dr.Konda Hari Krishna and has been published by Leilani Katie Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-03 with Computers categories.
Dr.Konda Hari Krishna, Associate Professor, Department of CSE, School of Computing, Mohan Babu University, Tirupati, Andhra Pradesh, India. Ms.S.Thulasi Bharathi, Assistant Professor, Department of Computer Science, St. Joseph’s College (Autonomous), Tiruchirappalli, Tamil Nadu, India Dr.N.Thinaharan, Assistant Professor, Department of Computer Science, Thanthai Hans Roever College (Autonomous), Perambalur, Tamil Nadu, India. Dr.Bhavani.K, Professor, Institute of CSE, Department of Spatial Informatics, Saveetha School of Engineering, SIMATS University, Chennai, Tamil Nadu, India.
Deep Learning For Natural Language Processing
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Author : Palash Goyal
language : en
Publisher: Apress
Release Date : 2018-06-26
Deep Learning For Natural Language Processing written by Palash Goyal and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-26 with Computers categories.
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.
Deep Learning For Coders With Fastai And Pytorch
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Author : Jeremy Howard
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-29
Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Transfer Learning For Natural Language Processing
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Author : Paul Azunre
language : en
Publisher: Simon and Schuster
Release Date : 2021-08-31
Transfer Learning For Natural Language Processing written by Paul Azunre 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 2021-08-31 with Computers categories.
Build custom NLP models in record time by adapting pre-trained machine learning models to solve specialized problems. Summary In Transfer Learning for Natural Language Processing you will learn: Fine tuning pretrained models with new domain data Picking the right model to reduce resource usage Transfer learning for neural network architectures Generating text with generative pretrained transformers Cross-lingual transfer learning with BERT Foundations for exploring NLP academic literature Training deep learning NLP models from scratch is costly, time-consuming, and requires massive amounts of data. In Transfer Learning for Natural Language Processing, DARPA researcher Paul Azunre reveals cutting-edge transfer learning techniques that apply customizable pretrained models to your own NLP architectures. You’ll learn how to use transfer learning to deliver state-of-the-art results for language comprehension, even when working with limited label data. Best of all, you’ll save on training time and computational costs. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build custom NLP models in record time, even with limited datasets! Transfer learning is a machine learning technique for adapting pretrained machine learning models to solve specialized problems. This powerful approach has revolutionized natural language processing, driving improvements in machine translation, business analytics, and natural language generation. About the book Transfer Learning for Natural Language Processing teaches you to create powerful NLP solutions quickly by building on existing pretrained models. This instantly useful book provides crystal-clear explanations of the concepts you need to grok transfer learning along with hands-on examples so you can practice your new skills immediately. As you go, you’ll apply state-of-the-art transfer learning methods to create a spam email classifier, a fact checker, and more real-world applications. What's inside Fine tuning pretrained models with new domain data Picking the right model to reduce resource use Transfer learning for neural network architectures Generating text with pretrained transformers About the reader For machine learning engineers and data scientists with some experience in NLP. About the author Paul Azunre holds a PhD in Computer Science from MIT and has served as a Principal Investigator on several DARPA research programs. Table of Contents PART 1 INTRODUCTION AND OVERVIEW 1 What is transfer learning? 2 Getting started with baselines: Data preprocessing 3 Getting started with baselines: Benchmarking and optimization PART 2 SHALLOW TRANSFER LEARNING AND DEEP TRANSFER LEARNING WITH RECURRENT NEURAL NETWORKS (RNNS) 4 Shallow transfer learning for NLP 5 Preprocessing data for recurrent neural network deep transfer learning experiments 6 Deep transfer learning for NLP with recurrent neural networks PART 3 DEEP TRANSFER LEARNING WITH TRANSFORMERS AND ADAPTATION STRATEGIES 7 Deep transfer learning for NLP with the transformer and GPT 8 Deep transfer learning for NLP with BERT and multilingual BERT 9 ULMFiT and knowledge distillation adaptation strategies 10 ALBERT, adapters, and multitask adaptation strategies 11 Conclusions
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.
Natural Language Processing In Artificial Intelligence
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Author : Brojo Kishore Mishra
language : en
Publisher: Apple Academic Press
Release Date : 2022-06
Natural Language Processing In Artificial Intelligence written by Brojo Kishore Mishra and has been published by Apple Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06 with categories.
Natural Language Processing in Artificial Intelligence, focuses on natural language processing, artificial intelligence, and allied areas. The book delves into natural language processing, which enables communication between people and computers and automatic translation to facilitate easy interaction with others around the world.
Proceedings Of The International Conference On Artificial Intelligence And Cloud Icaic 25
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Author :
language : en
Publisher: Leilani Katie Publication
Release Date : 2025-05-17
Proceedings Of The International Conference On Artificial Intelligence And Cloud Icaic 25 written by and has been published by Leilani Katie Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-17 with Computers categories.
Dr.A.Bamini, Assistant Professor and Head, Department of Computer Applications, The Standard Fireworks Rajaratnam College for Women (Autonomous), Sivakasi, Tamil Nadu, India. Mrs.P.Muthulakshmi, Assistant Professor, Department of Computer Applications, The Standard Fireworks Rajaratnam College for Women (Autonomous), Sivakasi, Tamil Nadu, India. Mrs.V.Vanthana, Assistant Professor, Department of Computer Applications, The Standard Fireworks Rajaratnam College for Women (Autonomous), Sivakasi, Tamil Nadu, India.
Artificial Intelligence In Healthcare
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Author : Adam Bohr
language : en
Publisher: Academic Press
Release Date : 2020-06-21
Artificial Intelligence In Healthcare written by Adam Bohr and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-21 with Computers categories.
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
The Future Of Artificial Intelligence
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Author : R.H Rizvi
language : en
Publisher: R.H Rizvi
Release Date : 2024-06-14
The Future Of Artificial Intelligence written by R.H Rizvi and has been published by R.H Rizvi this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-14 with Computers categories.
The Future of Artificial Intelligence delves into the transformative potential and profound implications of artificial intelligence on various facets of human life and industry. This comprehensive book explores the historical evolution of AI, the core technologies driving advancements, and the diverse applications across sectors such as healthcare, finance, education, and transportation. Each chapter meticulously examines the opportunities AI presents for innovation and societal impact, while also addressing the ethical considerations, privacy concerns, and economic disruptions associated with its rapid development. By integrating insights from experts and case studies, this book provides a balanced perspective on the promise and challenges of AI, offering readers a thoughtful analysis of how AI can shape a sustainable and inclusive future.
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