Found insideEach chapter consists of several recipes needed to complete a single project, such as training a music recommending system. Author Douwe Osinga also provides a chapter with half a dozen techniques to help you if you’re stuck. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. Includes 2 diskettes (for the Macintosh) Found insideThis book constitutes the refereed proceedings of the 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020, which was planned to take place in Ottawa, ON, Canada. Found inside – Page 1But 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? Found insideTopics covered in this volume include discourse theory, mechanical translation, deliberate writing, and revision. Natural Language Generation Systems contains contributions by leading researchers in the field. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Chapter 7. Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn ... Found insideDeep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Found inside – Page 215At the end of the chapter, we learned about the seq2seq model, which maps an input ... github.com/tensorlayer/seq2seq- chatbot Text summarization using a ... Found inside – Page 43NLP datasets Description Link https://github.com/abisee/cnndailymail It is the text summarization dataset which as two features namely the documents need to ... Found inside – Page 222Build effective real-world NLP applications using NER, RNNs, seq2seq models, ... called summarize_quietly(), is used to summarize pieces of text without ... Found insideThis book presents past and current research in text simplification, exploring key issues including automatic readability assessment, lexical simplification, and syntactic simplification. The text synthesizes and distills a broad and diverse research literature, linking contemporary machine learning techniques with the field's linguistic and computational foundations. The Long Short-Term Memory network, or LSTM for short, is a type of recurrent neural network that achieves state-of-the-art results on challenging prediction problems. Found insideThis book will get you up and running with one of the most cutting-edge deep learning libraries—PyTorch. Found inside – Page 161 Introduction Text summarization is a task to condense a piece of text to ... Recently, sequenceto-sequence (seq2seq) models [19] provide an effective new ... Found insideUsing 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 ... Found insideIn light of the rapid rise of new trends and applications in various natural language processing tasks, this book presents high-quality research in the field. Found insideThis book constitutes the refereed proceedings of the 8th Conference on Artificial Intelligence and Natural Language, AINL 2019, held in Tartu, Estonia, in November 2019. Found inside – Page iThis book constitutes the refereed proceedings of the 24th International Conference on Applications of Natural Language to Information Systems, NLDB 2019, held in Salford, UK, in June 2019. Found inside – Page 182The attention mechanism has been successfully applied to various text ... summarization.21 We provide Python code implementing all the seq2seq models ... Found insideSound understanding of the fundamentals of deep learning will be helpful. This book is an introduction to deep RL and requires no background in RL Introduces regular expressions and how they are used, discussing topics including metacharacters, nomenclature, matching and modifying text, expression processing, benchmarking, optimizations, and loops. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field ... Found inside – Page iWhile 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 ... After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks. Until now there has been no state-of-the-art collection of the most important writings in automatic text summarization. This book presents the key developments in the field in an integrated framework and suggests future research areas. This volume contains the proceedings of the 3rd International Conference on AdvancesinInformationSystems(ADVIS)heldinIzmir,Turkey,20–22October, 2004. This was the third conference dedicated to the memory of Prof. Esen Ozkarahan. Found insideThis book constitutes the proceedings of the 17th China National Conference on Computational Linguistics, CCL 2018, and the 6th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2018, ... Found inside – Page iThis open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international ... Found insideLearn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research. Neural Approaches to Conversational AI is a valuable resource for students, researchers, and software developers. Found insideThis two-volume set LNCS 12035 and 12036 constitutes the refereed proceedings of the 42nd European Conference on IR Research, ECIR 2020, held in Lisbon, Portugal, in April 2020.* The 55 full papers presented together with 8 reproducibility ... Found inside – Page iThe second edition of this book will show you how to use the latest state-of-the-art frameworks in NLP, coupled with Machine Learning and Deep Learning to solve real-world case studies leveraging the power of Python. Automatic Summarization is a comprehensive overview of research in summarization, including the more traditional efforts in sentence extraction as well as the most novel recent approaches for determining important content, for domain and ... Found insideAbout the Book Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you'll use readily available Python packages to capture the meaning in text and react accordingly. A major part of natural language processing now depends on the use of text data to build linguistic analyzers. Found insideThis book presents the fundamentals and advances in the field of data visualization and knowledge engineering, supported by case studies and practical examples. Found insideNeural networks are a family of powerful machine learning models and this book focuses on their application to natural language data. You should have basic OpenCV and C/C++ programming experience before reading this book, as it is aimed at Computer Science graduates, researchers, and computer vision experts widening their expertise. Found insideThis book is about making machine learning models and their decisions interpretable. Found inside – Page iAfter reading this book you will have an overview of the exciting field of deep neural networks and an understanding of most of the major applications of deep learning. This book demonstrates a set of simple to complex problems you may encounter while building machine learning models. Found inside – Page 1In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. This book has numerous coding exercises that will help you to quickly deploy natural language processing techniques, such as text classification, parts of speech identification, topic modeling, text summarization, text generation, entity ... Found insideThis book constitutes the refereed proceedings of the 16th International Conference of the Pacific Association for Computational Linguistics, PACLING 2019, held in Hanoi, Vietnam, in October 2019. 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