Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Evaluacion De Las Librerias De R Para Mineria De Textos En Castellano PDF full book. Access full book title Evaluacion De Las Librerias De R Para Mineria De Textos En Castellano.
| Author | : Natalia Garzón Méndez |
| Publisher | : |
| Total Pages | : 49 |
| Release | : 2019 |
| Genre | : |
| ISBN | : |
| Author | : Yihui Xie |
| Publisher | : CRC Press |
| Total Pages | : 140 |
| Release | : 2016-12-12 |
| Genre | : Mathematics |
| ISBN | : 1351792601 |
bookdown: Authoring Books and Technical Documents with R Markdown presents a much easier way to write books and technical publications than traditional tools such as LaTeX and Word. The bookdown package inherits the simplicity of syntax and flexibility for data analysis from R Markdown, and extends R Markdown for technical writing, so that you can make better use of document elements such as figures, tables, equations, theorems, citations, and references. Similar to LaTeX, you can number and cross-reference these elements with bookdown. Your document can even include live examples so readers can interact with them while reading the book. The book can be rendered to multiple output formats, including LaTeX/PDF, HTML, EPUB, and Word, thus making it easy to put your documents online. The style and theme of these output formats can be customized. We used books and R primarily for examples in this book, but bookdown is not only for books or R. Most features introduced in this book also apply to other types of publications: journal papers, reports, dissertations, course handouts, study notes, and even novels. You do not have to use R, either. Other choices of computing languages include Python, C, C++, SQL, Bash, Stan, JavaScript, and so on, although R is best supported. You can also leave out computing, for example, to write a fiction. This book itself is an example of publishing with bookdown and R Markdown, and its source is fully available on GitHub.
| Author | : World Bank |
| Publisher | : World Bank Publications |
| Total Pages | : 468 |
| Release | : 1996 |
| Genre | : Business & Economics |
| ISBN | : |
This edition of the World Bank has been revised and expanded by the Terminology Unit in the Languages Services Division of the World Bank in collaboration with the English, Spanish, and French Translation Sections. The Glossary is intended to assist the Bank's translators and interpreters, other Bank staff using French and Spanish in their work, and free-lance translator's and interpreters employed by the Bank. For this reason, the Glossary contains not only financial and economic terminology and terms relating to the Bank's procedures and practices, but also terms that frequently occur in Bank documents, and others for which the Bank has a preferred equivalent. Although many of these terms, relating to such fields as agriculture, education, energy, housing, law, technology, and transportation, could be found in other sources, they have been assembled here for ease of reference. A list of acronyms occurring frequently in Bank texts (the terms to which they refer being found in the Glossary) and a list of international, regional, and national organizations will be found at the end of the Glossary.
| Author | : Brad Boehmke |
| Publisher | : CRC Press |
| Total Pages | : 374 |
| Release | : 2019-11-07 |
| Genre | : Business & Economics |
| ISBN | : 1000730433 |
Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.
| Author | : Greg Wilson |
| Publisher | : CRC Press |
| Total Pages | : 250 |
| Release | : 2019-10-08 |
| Genre | : Computers |
| ISBN | : 1000728153 |
Hundreds of grassroots groups have sprung up around the world to teach programming, web design, robotics, and other skills outside traditional classrooms. These groups exist so that people don't have to learn these things on their own, but ironically, their founders and instructors are often teaching themselves how to teach. There's a better way. This book presents evidence-based practices that will help you create and deliver lessons that work and build a teaching community around them. Topics include the differences between different kinds of learners, diagnosing and correcting misunderstandings, teaching as a performance art, what motivates and demotivates adult learners, how to be a good ally, fostering a healthy community, getting the word out, and building alliances with like-minded groups. The book includes over a hundred exercises that can be done individually or in groups, over 350 references, and a glossary to help you navigate educational jargon.
| Author | : Zura Kakushadze |
| Publisher | : Springer |
| Total Pages | : 480 |
| Release | : 2018-12-13 |
| Genre | : Business & Economics |
| ISBN | : 3030027929 |
The book provides detailed descriptions, including more than 550 mathematical formulas, for more than 150 trading strategies across a host of asset classes and trading styles. These include stocks, options, fixed income, futures, ETFs, indexes, commodities, foreign exchange, convertibles, structured assets, volatility, real estate, distressed assets, cash, cryptocurrencies, weather, energy, inflation, global macro, infrastructure, and tax arbitrage. Some strategies are based on machine learning algorithms such as artificial neural networks, Bayes, and k-nearest neighbors. The book also includes source code for illustrating out-of-sample backtesting, around 2,000 bibliographic references, and more than 900 glossary, acronym and math definitions. The presentation is intended to be descriptive and pedagogical and of particular interest to finance practitioners, traders, researchers, academics, and business school and finance program students.
| Author | : Christoph Molnar |
| Publisher | : Lulu.com |
| Total Pages | : 320 |
| Release | : 2020 |
| Genre | : Artificial intelligence |
| ISBN | : 0244768528 |
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
| Author | : |
| Publisher | : |
| Total Pages | : |
| Release | : 2015-01-05 |
| Genre | : |
| ISBN | : 9781622701070 |
The Cal/OSHA Pocket Guide for the Construction Industry is a handy guide for workers, employers, supervisors, and safety personnel. This latest 2011 edition is a quick field reference that summarizes selected safety standards from the California Code of Regulations. The major subject headings are alphabetized and cross-referenced within the text, and it has a detailed index. Spiral bound, 8.5 x 5.5"
| Author | : Elisabeth Métais |
| Publisher | : Springer |
| Total Pages | : 0 |
| Release | : 2014-08-05 |
| Genre | : Computers |
| ISBN | : 9783319079820 |
This book constitutes the refereed proceedings of the 19th International Conference on Applications of Natural Language to Information Systems, NLDB 2014, held in Montpellier, France, in June 2014. The 13 long papers, 8 short papers, 14 poster papers, and 7 demo papers presented together with 2 invited talks in this volume were carefully reviewed and selected from 73 submissions. The papers cover the following topics: syntactic, lexical and semantic analysis; information extraction; information retrieval and sentiment analysis and social networks.
| Author | : Emil Hvitfeldt |
| Publisher | : CRC Press |
| Total Pages | : 402 |
| Release | : 2021-10-22 |
| Genre | : Computers |
| ISBN | : 1000461971 |
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.
