Machine Reading Comprehension with Deep Neural Networks
| Author | : Zongheng Wu |
| Publisher | : |
| Total Pages | : 51 |
| Release | : 2019 |
| Genre | : Artificial intelligence |
| ISBN | : |
Machine Reading Comprehension With Deep Neural Networks
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Machine Reading Comprehension
| Author | : Chenguang Zhu |
| Publisher | : Elsevier |
| Total Pages | : 272 |
| Release | : 2021-03-20 |
| Genre | : Computers |
| ISBN | : 0323901190 |
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
Strengthening Deep Neural Networks
| Author | : Katy Warr |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 246 |
| Release | : 2019-07-03 |
| Genre | : Computers |
| ISBN | : 1492044903 |
As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—are used daily to process image, audio, and video data. Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you’re a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attack, or someone fascinated by the differences between artificial and biological perception, this book is for you. Delve into DNNs and discover how they could be tricked by adversarial input Investigate methods used to generate adversarial input capable of fooling DNNs Explore real-world scenarios and model the adversarial threat Evaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial data Examine some ways in which AI might become better at mimicking human perception in years to come
Neural Reading Comprehension and Beyond
| Author | : Danqi Chen |
| Publisher | : |
| Total Pages | : |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
Teaching machines to understand human language documents is one of the most elusive and long-standing challenges in Artificial Intelligence. This thesis tackles the problem of reading comprehension: how to build computer systems to read a passage of text and answer comprehension questions. On the one hand, we think that reading comprehension is an important task for evaluating how well computer systems understand human language. On the other hand, if we can build high-performing reading comprehension systems, they would be a crucial technology for applications such as question answering and dialogue systems. In this thesis, we focus on neural reading comprehension: a class of reading comprehension models built on top of deep neural networks. Compared to traditional sparse, hand-designed feature-based models, these end-to-end neural models have proven to be more effective in learning rich linguistic phenomena and improved performance on all the modern reading comprehension benchmarks by a large margin. This thesis consists of two parts. In the first part, we aim to cover the essence of neural reading comprehension and present our efforts at building effective neural reading comprehension models, and more importantly, understanding what neural reading comprehension models have actually learned, and what depth of language understanding is needed to solve current tasks. We also summarize recent advances and discuss future directions and open questions in this field. In the second part of this thesis, we investigate how we can build practical applications based on the recent success of neural reading comprehension. In particular, we pioneered two new research directions: 1) how we can combine information retrieval techniques with neural reading comprehension to tackle large-scale open-domain question answering; and 2) how we can build conversational question answering systems from current single-turn, span-based reading comprehension models. We implemented these ideas in the DrQA and CoQA projects and we demonstrate the effectiveness of these approaches. We believe that they hold great promise for future language technologies.
Deep Learning for Natural Language Processing
| Author | : Karthiek Reddy Bokka |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 372 |
| Release | : 2019-06-11 |
| Genre | : Computers |
| ISBN | : 1838553673 |
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 Neural Network Applications
| Author | : Hasmik Osipyan |
| Publisher | : CRC Press |
| Total Pages | : 158 |
| Release | : 2022-04-28 |
| Genre | : Computers |
| ISBN | : 0429556209 |
The world is on the verge of fully ushering in the fourth industrial revolution, of which artificial intelligence (AI) is the most important new general-purpose technology. Like the steam engine that led to the widespread commercial use of driving machineries in the industries during the first industrial revolution; the internal combustion engine that gave rise to cars, trucks, and airplanes; electricity that caused the second industrial revolution through the discovery of direct and alternating current; and the Internet, which led to the emergence of the information age, AI is a transformational technology. It will cause a paradigm shift in the way’s problems are solved in every aspect of our lives, and, from it, innovative technologies will emerge. AI is the theory and development of machines that can imitate human intelligence in tasks such as visual perception, speech recognition, decision-making, and human language translation. This book provides a complete overview on the deep learning applications and deep neural network architectures. It also gives an overview on most advanced future-looking fundamental research in deep learning application in artificial intelligence. Research overview includes reasoning approaches, problem solving, knowledge representation, planning, learning, natural language processing, perception, motion and manipulation, social intelligence and creativity. It will allow the reader to gain a deep and broad knowledge of the latest engineering technologies of AI and Deep Learning and is an excellent resource for academic research and industry applications.
Interpretability in Deep Learning
| Author | : Ayush Somani |
| Publisher | : Springer Nature |
| Total Pages | : 483 |
| Release | : 2023-06-01 |
| Genre | : Computers |
| ISBN | : 3031206398 |
This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of computer vision, optics and machine learning related topic. The book can be used as a monograph on interpretability in deep learning covering the most recent topics as well as a textbook for graduate students. Scientists with research, development and application responsibilities benefit from its systematic exposition.
Supervised Machine Learning for Text Analysis in R
| Author | : Emil Hvitfeldt |
| Publisher | : CRC Press |
| Total Pages | : 369 |
| Release | : 2021-10-22 |
| Genre | : Computers |
| ISBN | : 1000461998 |
Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.
Advances in Machine Learning/Deep Learning-based Technologies
| Author | : George A. Tsihrintzis |
| Publisher | : Springer Nature |
| Total Pages | : 237 |
| Release | : 2021-08-05 |
| Genre | : Technology & Engineering |
| ISBN | : 3030767949 |
As the 4th Industrial Revolution is restructuring human societal organization into, so-called, “Society 5.0”, the field of Machine Learning (and its sub-field of Deep Learning) and related technologies is growing continuously and rapidly, developing in both itself and towards applications in many other disciplines. Researchers worldwide aim at incorporating cognitive abilities into machines, such as learning and problem solving. When machines and software systems have been enhanced with Machine Learning/Deep Learning components, they become better and more efficient at performing specific tasks. Consequently, Machine Learning/Deep Learning stands out as a research discipline due to its worldwide pace of growth in both theoretical advances and areas of application, while achieving very high rates of success and promising major impact in science, technology and society. The book at hand aims at exposing its readers to some of the most significant Advances in Machine Learning/Deep Learning-based Technologies. The book consists of an editorial note and an additional ten (10) chapters, all invited from authors who work on the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into five parts, namely (i) Machine Learning/Deep Learning in Socializing and Entertainment, (ii) Machine Learning/Deep Learning in Education, (iii) Machine Learning/Deep Learning in Security, (iv) Machine Learning/Deep Learning in Time Series Forecasting, and (v) Machine Learning in Video Coding and Information Extraction. This research book is directed towards professors, researchers, scientists, engineers and students in Machine Learning/Deep Learning-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent Machine Learning/Deep Learning-based technologies. An extensive list of bibliographic references at the end of each chapter guides the readers to probe further into the application areas of interest to them.
