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РаÑпознавание ÑаÑÑей Ñела Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð½ÐµÐ¹ÑоннÑÑ ÑеÑей
2019ÐÐ°Ð½Ð½Ð°Ñ ÑабоÑа оÑноÑиÑÑÑ Ðº ÑÑеÑе пÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð¸ обÑÑÐµÐ½Ð¸Ñ Ð½ÐµÐ¹ÑоннÑÑ ÑеÑей Ð´Ð»Ñ ÑеÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°Ñ, ÑвÑзаннÑÑ Ñ ÐºÐ¾Ð¼Ð¿ÑÑÑеÑнÑм зÑением. РпеÑвой главе пÑоизводиÑÑÑ Ð¾Ð±Ð·Ð¾Ñ Ð¾Ð±ÑÐ¸Ñ Ð¿Ð¾Ð½ÑÑий нейÑоннÑÑ ÑеÑей и Ð¸Ñ ...
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2019
Рданной ÑабоÑе изложена ÑÑÑноÑÑÑ Ð¿Ð¾Ð´Ñ Ð¾Ð´Ð° к пÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð½ÐµÐ¹ÑоннÑÑ ÑеÑей глÑбокого обÑÑениÑ. ÐÐ°Ð½Ñ Ð¾Ð±Ñие понÑÑÐ¸Ñ rtf-обÑекÑов, LSTM ÑеÑей и модели Se-quence to sequence.
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Рданной ÑабоÑе изложена ÑÑÑноÑÑÑ Ð¿Ð¾Ð´Ñ Ð¾Ð´Ð° к пÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð½ÐµÐ¹ÑоннÑÑ ÑеÑей глÑбокого обÑÑениÑ. ÐÐ°Ð½Ñ Ð¾Ð±Ñие понÑÑÐ¸Ñ rtf-обÑекÑов, LSTM ÑеÑей и модели Se-quence to sequence.
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2023
Рданной ÑабоÑе ÑокÑÑиÑÑеÑÑÑ Ð²Ð½Ð¸Ð¼Ð°Ð½Ð¸Ðµ на ÑазÑабоÑке нейÑоннÑÑ ÑеÑей, ÑпеÑиалÑно пÑедназнаÑеннÑÑ Ð´Ð»Ñ ÑÑибологиÑеÑÐºÐ¸Ñ ÑкÑпеÑименÑов. ТÑибологиÑеÑкие ÑкÑпеÑименÑÑ ÑвлÑÑÑÑÑ Ð½ÐµÐ¾ÑÑемлемой ÑаÑÑÑ ...
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Рданной ÑабоÑе ÑокÑÑиÑÑеÑÑÑ Ð²Ð½Ð¸Ð¼Ð°Ð½Ð¸Ðµ на ÑазÑабоÑке нейÑоннÑÑ ÑеÑей, ÑпеÑиалÑно пÑедназнаÑеннÑÑ Ð´Ð»Ñ ÑÑибологиÑеÑÐºÐ¸Ñ ÑкÑпеÑименÑов. ТÑибологиÑеÑкие ÑкÑпеÑименÑÑ ÑвлÑÑÑÑÑ Ð½ÐµÐ¾ÑÑемлемой ÑаÑÑÑ ...
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2023
This paper discusses the problem of forming studentsâ digital image in the educational environment of the university and their ignorance of the opportunities that the management of social networks and portfolio can provide them. The tasks that were solved in the course of the study: to analyze the theoretical base of image and digital image; identify
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This paper discusses the problem of forming studentsâ digital image in the educational environment of the university and their ignorance of the opportunities that the management of social networks and portfolio can provide them. The tasks that were solved in the course of the study: to analyze the theoretical base of image and digital image; identify
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Ðодели нейÑонов в ÑпайковÑÑ Ð½ÐµÐ¹ÑоннÑÑ ÑеÑÑÑ
Ð ÑÑебном поÑобии ÑаÑÑмаÑÑиваÑÑÑÑ Ð²Ð¾Ð¿ÑоÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¸Ð¼Ð¿ÑлÑÑного (Ñпайкового) нейÑона как ÑлеменÑа биоподобнÑÑ Ð¿Ð¾ ÑÑÑÑкÑÑÑе и ÑÑнкÑиÑм ÑпайковÑÑ Ð½ÐµÐ¹ÑоннÑÑ ÑеÑей.openaire +1 more source
2022
Тема вÑпÑÑкной квалиÑикаÑионной ÑабоÑÑ: «ÐÑенка ÑÑÑекÑов гипеÑпаÑамеÑÑов в ÑвеÑÑоÑнÑÑ Ð½ÐµÐ¹ÑоннÑÑ ÑеÑÑÑ Â». ÐÐ°Ð½Ð½Ð°Ñ ÑабоÑа поÑвÑÑена иÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÑÑекÑов гипеÑпаÑамеÑÑов нейÑоннÑÑ ÑеÑей в ÑÐ²ÐµÑ ...
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Тема вÑпÑÑкной квалиÑикаÑионной ÑабоÑÑ: «ÐÑенка ÑÑÑекÑов гипеÑпаÑамеÑÑов в ÑвеÑÑоÑнÑÑ Ð½ÐµÐ¹ÑоннÑÑ ÑеÑÑÑ Â». ÐÐ°Ð½Ð½Ð°Ñ ÑабоÑа поÑвÑÑена иÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÑÑекÑов гипеÑпаÑамеÑÑов нейÑоннÑÑ ÑеÑей в ÑÐ²ÐµÑ ...
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This work is devoted to the study of psychological separation from parents and coping strategies of students at higher educational institutions in St. Petersburg. During the study, an analysis was carried out of the severity of the components of separation from parents, coping mechanisms, and characteristics of socio-psychological adaptation. Were used
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2020
Тема вÑпÑÑкной квалиÑикаÑионной ÑабоÑÑ: «ÐаÑегоÑÐ¸Ñ ÑвиденÑиалÑноÑÑи в ÑÑÑÑком и киÑайÑком ÑзÑÐºÐ°Ñ Â». ÐÑÐµÐ´Ð¼ÐµÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ â ÑпоÑÐ¾Ð±Ñ Ð°ÐºÑÑализаÑии каÑегоÑии ÑвиденÑиалÑноÑÑи в коÑпÑÑÐ°Ñ ÑÑÑÑ ...
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Тема вÑпÑÑкной квалиÑикаÑионной ÑабоÑÑ: «ÐаÑегоÑÐ¸Ñ ÑвиденÑиалÑноÑÑи в ÑÑÑÑком и киÑайÑком ÑзÑÐºÐ°Ñ Â». ÐÑÐµÐ´Ð¼ÐµÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ â ÑпоÑÐ¾Ð±Ñ Ð°ÐºÑÑализаÑии каÑегоÑии ÑвиденÑиалÑноÑÑи в коÑпÑÑÐ°Ñ ÑÑÑÑ ...
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2003
ÐоÑобие ÑооÑвеÑÑÑвÑÐµÑ Ð³Ð¾ÑÑдаÑÑÑвеннÑм обÑазоваÑелÑнÑм ÑÑандаÑÑам диÑÑиплин «ÐокÑменÑоведение» (ÐÐÐ.Ф.08) и «ÐаÑиÑа и обÑабоÑка конÑиденÑиалÑнÑÑ Ð´Ð¾ÐºÑменÑов (ÐÐÐ.Ф.12) подгоÑовки ÑпеÑиалиÑÑов Ð ...
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ÐоÑобие ÑооÑвеÑÑÑвÑÐµÑ Ð³Ð¾ÑÑдаÑÑÑвеннÑм обÑазоваÑелÑнÑм ÑÑандаÑÑам диÑÑиплин «ÐокÑменÑоведение» (ÐÐÐ.Ф.08) и «ÐаÑиÑа и обÑабоÑка конÑиденÑиалÑнÑÑ Ð´Ð¾ÐºÑменÑов (ÐÐÐ.Ф.12) подгоÑовки ÑпеÑиалиÑÑов Ð ...
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2020
The paper considers a machine learning algorithm designed to solve the classification of cancerous tumors. The algorithm is based on the concept of Siamese convolutional neural networks (SCNN). SCNN is trained on computed tomography images of the lung, which are presented in the form of three-dimensional data arrays. SCNN translates the original images
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The paper considers a machine learning algorithm designed to solve the classification of cancerous tumors. The algorithm is based on the concept of Siamese convolutional neural networks (SCNN). SCNN is trained on computed tomography images of the lung, which are presented in the form of three-dimensional data arrays. SCNN translates the original images
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