Москва

Россия

Срочный перевод отдельных глав из диссертации Classification with Large Sparse Datasets. 
Обязательное владение лексикой в области data science и ясный грамотный русский язык. 
Срок – четверг, 15 марта. Объем – исходя из вашей скорости. 
Оплата 350 руб. / стр., больше при быстром выполнении задания. 
Присылайте тестовый перевод на [url=mailto:atoskin@btlabs.ru]atoskin@btlabs.ru[/url] с темой письма "Переводчик с weblancer.net". 

Тестовый перевод. 
Large and sparse datasets are prevalent in the big data era. Specifically, with the rapid emergence of online services and digital devices, tons of data that track our selections, usages and feedback are generated and stored. Meanwhile, as more items are being digitized, these user behavior datasets become sparser and sparser. For example, the famous Netflix dataset contains 100, 480, 507 ratings given by 480, 189 users to 17, 770 movies, where 98.822% of the rating values are missin g. Apart from rating datasets, there are many more examples of large sparse data such as our likes on posts, photos and videos; our purchases on online shopping website; our clicks on webpages or advertisements, and so forth. 
Classification is one of the most fundamental data analysis technique and is of great importance in mining and making use of the large sparse data. For example, a company may need to classify the profitability of a product/service based on sparse user feedback vectors. For the public datasets shown in Table 1.1, various types of data labels (shown in the last column) are also provided for building interesting and useful classifiers. Besides, in the literature, much previous work has demonstrated that sparse data are highly useful for predictive modeling. For example, Brian et al. [14] have demonstrated that classifying user web browsing data, which is high-dimensional and sparse, is an effective solution for online display advertising.

Срочный перевод отдельных глав из диссертации Classification with Large Sparse Datasets. 
Обязательное владение лексикой в области data science и ясный грамотный русский язык. 
Срок – четверг, 15 марта. Объем – исходя из вашей скорости. 
Оплата 350 руб. / стр., больше при быстром выполнении задания. 
Присылайте тестовый перевод на atoskin@btlabs.ru с темой письма "Переводчик с <a href="https://www.weblancer.net">weblancer.net</a>". 

Тестовый перевод. 
Large and sparse datasets are prevalent in the big data era. Specifically, with the rapid emergence of online services and digital devices, tons of data that track our selections, usages and feedback are generated and stored. Meanwhile, as more items are being digitized, these user behavior datasets become sparser and sparser. For example, the famous Netflix dataset contains 100, 480, 507 ratings given by 480, 189 users to 17, 770 movies, where 98.822% of the rating values are missin g. Apart from rating datasets, there are many more examples of large sparse data such as our likes on posts, photos and videos; our purchases on online shopping website; our clicks on webpages or advertisements, and so forth. 
Classification is one of the most fundamental data analysis technique and is of great importance in mining and making use of the large sparse data. For example, a company may need to classify the profitability of a product/service based on sparse user feedback vectors. For the public datasets shown in Table 1.1, various types of data labels (shown in the last column) are also provided for building interesting and useful classifiers. Besides, in the literature, much previous work has demonstrated that sparse data are highly useful for predictive modeling. For example, Brian et al. [14] have demonstrated that classifying user web browsing data, which is high-dimensional and sparse, is an effective solution for online display advertising.

Александр btlabs

EN-RU. Письменный технический. Тема: анализ данных, машинное обучение