no matching distribution found for gensim

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Found inside – Page 25The initial steam rates for 2D cases with steam trap control match published chamber development depends on the ... that is , fluxes were computed from phase velocities , with a momentum there is no frictional pressure loss between the source - sink ... This code was incorporated in the Gensim lators will compute an average density based on entering fluid model ( 2 ) ... This leads to illPEER REVIEWED PAPER ( “ REVIEW AND PUBLICATION PROCESS " CAN BE FOUND ON OUR ... In this volume, readers immediately begin working with text, and each chapter examines a new technique or process, allowing readers to obtain a broad exposure to core R procedures and a fundamental understanding of the possibilities of ... Found insideWith this book, you will learn how to perform various machine learning tasks in different environments. Found insideThis open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. 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. In this book, this point is argued with rich and convincing evidence from virtually all fields of linguistics. Found insideAcquire and analyze data from all corners of the social web with Python About This Book Make sense of highly unstructured social media data with the help of the insightful use cases provided in this guide Use this easy-to-follow, step-by ... Found inside – Page 394... 22–24 GenSim software for semantic memory modeling, 246 Gershman, S.J., 187 Global matching models, 167–168 Go/No-Go Discrimination Task, 44, 349, ... Found insideThis book is your guide to master deep learning with TensorFlow, with the help of 10 real-world projects. Found insideThe fast-paced novel from the internationally bestselling author of The Best Laid Plans, Morning, Noon & Night and Bloodline. Authorship Attribution surveys the history and present state of the discipline, presenting some comparative results where available. 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 ... This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. Found insideThe key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning. Found inside – Page iWho This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Key Features This is the first book on pandas 1.x Practical, easy to implement recipes for quick solutions to common problems in data using pandas Master the fundamentals of pandas to quickly begin exploring any dataset Book Description The ... 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. Found insideThis book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems. Found insideIn this book, the authors survey and discuss recent and historical work on supervised and unsupervised learning of such alignments. Specifically, the book focuses on so-called cross-lingual word embeddings. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students. Found insideLeverage the power of machine learning and deep learning to extract information from text data About This Book Implement Machine Learning and Deep Learning techniques for efficient natural language processing Get started with NLTK and ... Found insideBecome an efficient data science practitioner by understanding Python's key concepts About This Book Quickly get familiar with data science using Python 3.5 Save time (and effort) with all the essential tools explained Create effective data ... Found insideThis open access book constitutes the proceedings of the 8th International Conference on Principles of Security and Trust, POST 2019, which took place in Prague, Czech Republic, in April 2019, held as part of the European Joint Conference ... Found inside – Page viiiOrganization of the book ○ Chapters 1–10: An absolute beginner with no ... how to download and install Anaconda distribution (and connected IPython and ... Written by a leading expert in the field, this unique volume contains current test design approaches and focuses only on software test design. Copeland illustrates each test design through detailed examples and step-by-step instructions. If you are a Scala, Java, or Python developer with an interest in machine learning and data analysis and are eager to learn how to apply common machine learning techniques at scale using the Spark framework, this is the book for you. Found inside – Page iThis book starts with the fundamentals of Spark and its evolution and then covers the entire spectrum of traditional machine learning algorithms along with natural language processing and recommender systems using PySpark. 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 inside – Page iiThis book: Provides complete coverage of the major concepts and techniques of natural language processing (NLP) and text analytics Includes practical real-world examples of techniques for implementation, such as building a text ... Found inside – Page 1With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data ... Found insideThe Python ecosystem with scikit-learn and pandas is required for operational machine learning. 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. Learn how to harness the powerful Python ecosystem and tools such as spaCy and Gensim to perform natural language processing, and computational linguistics algorithms. This first collection of selected articles from researchers in automatic analysis, storage, and use of terminology, and specialists in applied linguistics, computational linguistics, information retrieval, and artificial intelligence offers ... The volume constitutes a comprehensive and carefully planned collection of chapters that map out the most important areas of the adaptive Web, each solicited from the experts and leaders in the field. In this book, he also demonstrates a skill shared by the greatest radical thinkers: to reveal positions we've been taught to think of as extremism to be emanations of simple human decency and common sense. Found insideIn this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. 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 inside – Page 1About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Found inside – Page iThis book thoroughly addresses these and other considerations, leaving institutional investors and risk managers with a basis of knowledge that will enable them to extract the maximum value from alternative data. Found insideLearning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Found insideThis book teaches you to leverage deep learning models in performing various NLP tasks along with showcasing the best practices in dealing with the NLP challenges. The volume attests to the spreading of digital humanities in these fields and presents fundamental analysis of the rise of visual culture as well as specific test-cases concerning ancient manuscripts. The first comprehensive treatment of probabilistic Boolean networks, unifying different strands of current research and addressing emerging issues. 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