{"id":325807,"date":"2021-07-01T15:00:13","date_gmt":"2021-07-01T15:00:13","guid":{"rendered":"http:\/\/savepearlharbor.com\/?p=325807"},"modified":"-0001-11-30T00:00:00","modified_gmt":"-0001-11-29T21:00:00","slug":"","status":"publish","type":"post","link":"https:\/\/savepearlharbor.com\/?p=325807","title":{"rendered":"Data Phoenix Digest \u2014 01.07.2021"},"content":{"rendered":"\n<div class=\"post__text post__text_v2\" id=\"post-content-body\">\n<figure class=\"full-width\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/habrastorage.org\/getpro\/habr\/upload_files\/c2b\/871\/f73\/c2b871f73fbce8e1344a50e2e549434d.png\" width=\"1024\" height=\"512\"><figcaption><\/figcaption><\/figure>\n<p>\u041f\u0440\u0438\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u044e \u0432\u0441\u0435\u0445!<\/p>\n<p>\u0412\u0441\u0442\u0440\u0435\u0447\u0430\u0439\u0442\u0435 \u0441\u0432\u0435\u0436\u0438\u0439 \u0432\u044b\u043f\u0443\u0441\u043a&nbsp;<a href=\"https:\/\/dataphoenix.info\" rel=\"noopener noreferrer nofollow\">\u0434\u0430\u0439\u0434\u0436\u0435\u0441\u0442\u0430<\/a>&nbsp;\u043f\u043e\u043b\u0435\u0437\u043d\u044b\u0445 \u043c\u0430\u0442\u0435\u0440\u0438\u0430\u043b\u043e\u0432 \u0438\u0437 \u043c\u0438\u0440\u0430 Data Science &amp; Machine Learning \u0438 \u043d\u0435 \u0437\u0430\u0431\u044b\u0432\u0430\u0439\u0442\u0435 \u043f\u043e\u0434\u043f\u0438\u0441\u044b\u0432\u0430\u0442\u044c\u0441\u044f \u043d\u0430 \u043d\u0430\u0448&nbsp;<a href=\"https:\/\/t.me\/DataPhoenix\" rel=\"noopener noreferrer nofollow\"><u>Telegram-\u043a\u0430\u043d\u0430\u043b<\/u><\/a>.<\/p>\n<hr>\n<h3>\u0421\u0442\u0430\u0442\u044c\u0438<\/h3>\n<p><a href=\"https:\/\/machinelearningmastery.com\/xgboost-for-time-series-forecasting\/\" rel=\"noopener noreferrer nofollow\"><strong><u>How to Use XGBoost for Time Series Forecasting<\/u><\/strong><\/a> &#8212; \u043d\u0435\u0431\u043e\u043b\u044c\u0448\u043e\u0439 \u0442\u0443\u0442\u043e\u0440\u0438\u0430\u043b \u043e\u0431 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0438 XGBoost \u0434\u043b\u044f \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u0440\u044f\u0434\u043e\u0432<\/p>\n<p><a href=\"https:\/\/towardsdatascience.com\/genetic-algorithms-for-natural-language-processing-b055aa7c14e9\" rel=\"noopener noreferrer nofollow\"><strong><u>Genetic Algorithms for Natural Language Processing<\/u><\/strong><\/a> &#8212; \u0432\u0432\u043e\u0434\u043d\u0430\u044f \u0441\u0442\u0430\u0442\u044c\u044f \u043e \u0433\u0435\u043d\u0435\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0430\u0445 \u0438 \u043e\u0431 \u0438\u0445 \u0432\u0437\u0430\u0438\u043c\u043e\u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0438 \u0441 NLP \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0430\u043c\u0438.<\/p>\n<p><a href=\"https:\/\/machinelearningmastery.com\/differential-evolution-from-scratch-in-python\/\" rel=\"noopener noreferrer nofollow\"><strong><u>Differential Evolution from Scratch in Python<\/u><\/strong><\/a> &#8212; \u0441\u0442\u0430\u0442\u044c\u044f \u043e \u043c\u0435\u0442\u043e\u0434\u0430\u0445 \u043f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u044f \u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u043e\u0432 \u0434\u0438\u0444\u0444\u0435\u0440\u0435\u043d\u0446\u0438\u0430\u043b\u044c\u043d\u043e\u0439 \u044d\u0432\u043e\u043b\u044e\u0446\u0438\u0438 \u043d\u0430 Python.<\/p>\n<p><a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2021\/06\/style-your-pandas-dataframe-and-make-it-stunning\/\" rel=\"noopener noreferrer nofollow\"><strong><u>Style Your Pandas DataFrame and Make It Stunning<\/u><\/strong><\/a> &#8212; \u0442\u0435\u0445\u043d\u0438\u043a\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u0432\u0441\u0442\u0440\u043e\u0435\u043d\u043d\u044b\u0445 \u043c\u0435\u0442\u043e\u0434\u043e\u0432 \u0440\u0430\u0431\u043e\u0442\u044b \u0441 \u0434\u0430\u0442\u0430\u0444\u0440\u0435\u0439\u043c\u0430\u043c\u0438 \u043d\u0430 Pandas.<\/p>\n<p><a href=\"https:\/\/ai.facebook.com\/blog\/the-flores-101-data-set-helping-build-better-translation-systems-around-the-world\/\" rel=\"noopener noreferrer nofollow\"><strong><u>The FLORES-101 Data Set: Helping Build Better Translation Systems Around the World<\/u><\/strong><\/a> &#8212; \u043e\u0431\u0437\u043e\u0440 FLORES-101, \u043d\u043e\u0432\u043e\u0433\u043e \u043c\u0443\u043b\u044c\u0442\u0438\u0443\u0440\u043e\u0432\u043d\u0435\u0432\u043e\u0433\u043e \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u0430 \u0434\u043b\u044f \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u0431\u043e\u043b\u0435\u0435 \u0441\u043e\u0432\u0435\u0440\u0448\u0435\u043d\u043d\u044b\u0445 \u0441\u0438\u0441\u0442\u0435\u043c \u043f\u0435\u0440\u0435\u0432\u043e\u0434\u0430 \u043e\u0442 \u043a\u043e\u043c\u0430\u043d\u0434\u044b Facebook AI.<\/p>\n<h3>\u041d\u0430\u0443\u0447\u043d\u044b\u0435 \u0441\u0442\u0430\u0442\u044c\u0438<\/h3>\n<p><a href=\"https:\/\/nvlabs.github.io\/alias-free-gan\/\" rel=\"noopener noreferrer nofollow\"><strong><u>Alias-Free GAN<\/u><\/strong><\/a> &#8212; \u043d\u043e\u0432\u043e\u0435 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u0435 \u043e \u043f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u0438 \u043f\u0440\u043e\u0434\u0432\u0438\u043d\u0443\u0442\u044b\u0445 \u0433\u0435\u043d\u0435\u0440\u0430\u0442\u0438\u0432\u043d\u044b\u0445 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0434\u043b\u044f \u0440\u0430\u0431\u043e\u0442\u044b \u0441 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f\u043c\u0438, \u0432\u0438\u0434\u0435\u043e \u0438 \u0430\u043d\u0438\u043c\u0430\u0446\u0438\u0435\u0439 \u043e\u0442 NVIDIA.<\/p>\n<p><a href=\"https:\/\/arxiv.org\/abs\/2106.00666\" rel=\"noopener noreferrer nofollow\"><strong><u>You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection<\/u><\/strong><\/a> &#8212; \u0440\u0430\u0431\u043e\u0442\u0430 \u043e\u0442 \u043a\u0438\u0442\u0430\u0439\u0441\u043a\u0438\u0445 \u0443\u0447\u0435\u043d\u044b\u0445 \u043e\u0431 You Only Look at One Sequence (YOLOS), \u043d\u043e\u0432\u043e\u043c \u043d\u0430\u0431\u043e\u0440\u0435 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0434\u043b\u044f \u0434\u0435\u0442\u0435\u043a\u0446\u0438\u0438 \u043f\u0440\u0435\u0434\u043c\u0435\u0442\u043e\u0432.<\/p>\n<p><a href=\"https:\/\/arxiv.org\/abs\/2106.04803\" rel=\"noopener noreferrer nofollow\"><strong><u>CoAtNet: Marrying Convolution and Attention for All Data Sizes<\/u><\/strong><\/a> &#8212; \u043d\u0430\u0443\u0447\u043d\u0430\u044f \u0440\u0430\u0431\u043e\u0442\u0430 \u043e CoAtNets, \u043d\u043e\u0432\u043e\u043c \u0441\u0435\u043c\u0435\u0439\u0441\u0442\u0432\u0435 \u0433\u0438\u0431\u0440\u0438\u0434\u043d\u044b\u0445 \u0441\u0432\u0435\u0440\u0442\u043e\u0447\u043d\u044b\u0445 \u043c\u043e\u0434\u0435\u043b\u0435\u0439.<\/p>\n<p><a href=\"https:\/\/arxiv.org\/abs\/2106.05519\" rel=\"noopener noreferrer nofollow\"><strong><u>Consistent Instance False Positive Improves Fairness in Face Recognition<\/u><\/strong><\/a> &#8212; \u043e\u0431\u0437\u043e\u0440 \u043d\u043e\u0432\u043e\u0433\u043e \u043c\u0435\u0442\u043e\u0434\u0430 \u0443\u043c\u0435\u043d\u044c\u0448\u0435\u043d\u0438\u044f \u043b\u043e\u0436\u043d\u044b\u0445 \u0441\u0440\u0430\u0431\u0430\u0442\u044b\u0432\u0430\u043d\u0438\u0439 \u043f\u0440\u0438 \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u0438 \u043b\u0438\u0446.<\/p>\n<p><a href=\"https:\/\/arxiv.org\/abs\/2106.03823\" rel=\"noopener noreferrer nofollow\"><strong><u>Multivariate Probabilistic Regression with Natural Gradient Boosting<\/u><\/strong><\/a> &#8212; \u043d\u043e\u0432\u044b\u0439 \u044d\u0444\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u044b\u0439 \u043c\u0435\u0442\u043e\u0434 \u043c\u043d\u043e\u0433\u043e\u043c\u0435\u0440\u043d\u043e\u0439 \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u043d\u043e\u0439 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 \u0441 \u0435\u0441\u0442\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u044b\u043c \u0443\u0441\u0438\u043b\u0435\u043d\u0438\u0435\u043c \u0433\u0440\u0430\u0434\u0438\u0435\u043d\u0442\u0430.<\/p>\n<p><a href=\"https:\/\/dynamicvit.ivg-research.xyz\/\" rel=\"noopener noreferrer nofollow\"><strong><u>DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification<\/u><\/strong><\/a> &#8212; \u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a \u0434\u0438\u043d\u0430\u043c\u0438\u0447\u0435\u0441\u043a\u043e\u0433\u043e \u0440\u0430\u0437\u0431\u0438\u0435\u043d\u0438\u044f \u0442\u043e\u043a\u0435\u043d\u043e\u0432 \u0434\u043b\u044f \u043f\u043e\u0441\u0442\u0435\u043f\u0435\u043d\u043d\u043e\u0433\u043e \u0438 \u0434\u0438\u043d\u0430\u043c\u0438\u0447\u0435\u0441\u043a\u043e\u0433\u043e \u0443\u0434\u0430\u043b\u0435\u043d\u0438\u044f \u0438\u0437\u0431\u044b\u0442\u043e\u0447\u043d\u044b\u0445 \u0442\u043e\u043a\u0435\u043d\u043e\u0432 \u0432 \u0437\u0430\u0432\u0438\u0441\u0438\u043c\u043e\u0441\u0442\u0438 \u043e\u0442 \u0432\u0445\u043e\u0434\u043d\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445.<\/p>\n<h2>\u041f\u0440\u043e\u0435\u043a\u0442\u044b<\/h2>\n<p><a href=\"https:\/\/mlops.toys\/\" rel=\"noopener noreferrer nofollow\"><strong><u>MLOps Toys<\/u><\/strong><\/a> &#8212; \u043a\u0430\u0442\u0430\u043b\u043e\u0433 MLOps \u0442\u0443\u043b\u0437\u043e\u0432 \u0441 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u044c\u044e \u043f\u043e\u0438\u0441\u043a\u0430 \u043f\u043e \u043a\u0430\u0442\u0435\u0433\u043e\u0440\u0438\u044f\u043c.<\/p>\n<h3>\u0412\u0438\u0434\u0435\u043e<\/h3>\n<p><a href=\"https:\/\/www.youtube.com\/watch?v=tJ3v8h7A7RY\" rel=\"noopener noreferrer nofollow\"><strong><u>Data Governance<\/u><\/strong><\/a> &#8212; \u043f\u0440\u0435\u0434\u043c\u0435\u0442\u043d\u044b\u0439 \u0440\u0430\u0437\u0433\u043e\u0432\u043e\u0440 \u043e\u0431 \u0443\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0438 \u0434\u0430\u043d\u043d\u044b\u043c\u0438.<\/p>\n<p><a href=\"https:\/\/www.youtube.com\/watch?v=tZfN-8G0Yi0\" rel=\"noopener noreferrer nofollow\"><strong><u>Ingestion and Historization in the Data Lake<\/u><\/strong><\/a> &#8212; \u0434\u043e\u043a\u043b\u0430\u0434 \u043e \u043f\u0440\u0438\u0435\u043c\u0435 \u0438 \u0438\u0441\u0442\u043e\u0440\u0438\u0437\u0430\u0446\u0438\u0438 \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 \u043e\u0437\u0435\u0440\u0430\u0445 \u0434\u0430\u043d\u043d\u044b\u0445.<\/p>\n<hr>\n<p>\u0421\u043f\u0430\u0441\u0438\u0431\u043e, \u0447\u0442\u043e \u0434\u043e\u0447\u0438\u0442\u0430\u043b\u0438 \u044d\u0442\u043e\u0442 \u0432\u044b\u043f\u0443\u0441\u043a. \u041d\u0430\u0434\u0435\u044e\u0441\u044c, \u043a\u0430\u0436\u0434\u044b\u0439 \u043d\u0430\u0448\u0435\u043b \u0434\u043b\u044f \u0441\u0435\u0431\u044f \u0447\u0442\u043e-\u0442\u043e \u043f\u043e\u043b\u0435\u0437\u043d\u043e\u0435. \u0411\u0443\u0434\u0443 \u0431\u043b\u0430\u0433\u043e\u0434\u0430\u0440\u0435\u043d \u0437\u0430 \u043b\u044e\u0431\u044b\u0435 \u043f\u0440\u0435\u0434\u043b\u043e\u0436\u0435\u043d\u0438\u044f \u0434\u043b\u044f \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u0433\u043e \u0434\u0430\u0439\u0434\u0436\u0435\u0441\u0442\u0430.<\/p>\n<p>\u041f\u0440\u0438\u0441\u043e\u0435\u0434\u0438\u043d\u044f\u0439\u0442\u0435\u0441\u044c \u043a&nbsp;<a href=\"https:\/\/t.me\/DataPhoenix\" rel=\"noopener noreferrer nofollow\"><u>Telegram-\u043a\u0430\u043d\u0430\u043b\u0443<\/u><\/a>&nbsp;\u0434\u0430\u0439\u0434\u0436\u0435\u0441\u0442\u0430 \u0438 \u0435\u0433\u043e \u0441\u0442\u0440\u0430\u043d\u0438\u0446\u0430\u043c \u0432 \u0441\u043e\u0446\u0441\u0435\u0442\u044f\u0445: <a href=\"https:\/\/twitter.com\/Data_Phoenix\" rel=\"noopener noreferrer nofollow\"><u>Twitter<\/u><\/a>,&nbsp;<a href=\"https:\/\/www.facebook.com\/DataPhoenix.info\" rel=\"noopener noreferrer nofollow\"><u>Facebook<\/u><\/a>, \u0430 \u0442\u0430\u043a\u0436\u0435 \u043f\u043e\u0434\u043f\u0438\u0441\u044b\u0432\u0430\u0439\u0442\u0435\u0441\u044c \u043d\u0430 \u043d\u0430\u0448\u0443 \u0435\u0436\u0435\u043d\u0435\u0434\u0435\u043b\u044c\u043d\u0443\u044e <a href=\"https:\/\/dataphoenix.info\/subscribe\/\" rel=\"noopener noreferrer nofollow\">\u0440\u0430\u0441\u0441\u044b\u043b\u043a\u0443<\/a>.<\/p>\n<p>\u2190 <a href=\"https:\/\/habr.com\/ru\/post\/564484\/\" rel=\"noopener noreferrer nofollow\">\u041f\u0440\u0435\u0434\u044b\u0434\u0443\u0449\u0438\u0439 \u0432\u044b\u043f\u0443\u0441\u043a<\/a>.<\/p>\n<\/div>\n<p> \u0441\u0441\u044b\u043b\u043a\u0430 \u043d\u0430 \u043e\u0440\u0438\u0433\u0438\u043d\u0430\u043b \u0441\u0442\u0430\u0442\u044c\u0438 <a href=\"https:\/\/habr.com\/ru\/post\/565722\/\"> https:\/\/habr.com\/ru\/post\/565722\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"\n<div class=\"post__text post__text_v2\" id=\"post-content-body\">\n<figure class=\"full-width\"><figcaption><\/figcaption><\/figure>\n<p>\u041f\u0440\u0438\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u044e \u0432\u0441\u0435\u0445!<\/p>\n<p>\u0412\u0441\u0442\u0440\u0435\u0447\u0430\u0439\u0442\u0435 \u0441\u0432\u0435\u0436\u0438\u0439 \u0432\u044b\u043f\u0443\u0441\u043a&nbsp;<a href=\"https:\/\/dataphoenix.info\" rel=\"noopener noreferrer nofollow\">\u0434\u0430\u0439\u0434\u0436\u0435\u0441\u0442\u0430<\/a>&nbsp;\u043f\u043e\u043b\u0435\u0437\u043d\u044b\u0445 \u043c\u0430\u0442\u0435\u0440\u0438\u0430\u043b\u043e\u0432 \u0438\u0437 \u043c\u0438\u0440\u0430 Data Science &amp; Machine Learning \u0438 \u043d\u0435 \u0437\u0430\u0431\u044b\u0432\u0430\u0439\u0442\u0435 \u043f\u043e\u0434\u043f\u0438\u0441\u044b\u0432\u0430\u0442\u044c\u0441\u044f \u043d\u0430 \u043d\u0430\u0448&nbsp;<a href=\"https:\/\/t.me\/DataPhoenix\" rel=\"noopener noreferrer nofollow\"><u>Telegram-\u043a\u0430\u043d\u0430\u043b<\/u><\/a>.<\/p>\n<hr>\n<h3>\u0421\u0442\u0430\u0442\u044c\u0438<\/h3>\n<p><a href=\"https:\/\/machinelearningmastery.com\/xgboost-for-time-series-forecasting\/\" rel=\"noopener noreferrer nofollow\"><strong><u>How to Use XGBoost for Time Series Forecasting<\/u><\/strong><\/a> &#8212; \u043d\u0435\u0431\u043e\u043b\u044c\u0448\u043e\u0439 \u0442\u0443\u0442\u043e\u0440\u0438\u0430\u043b \u043e\u0431 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0438 XGBoost \u0434\u043b\u044f \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u0440\u044f\u0434\u043e\u0432<\/p>\n<p><a href=\"https:\/\/towardsdatascience.com\/genetic-algorithms-for-natural-language-processing-b055aa7c14e9\" rel=\"noopener noreferrer nofollow\"><strong><u>Genetic Algorithms for Natural Language Processing<\/u><\/strong><\/a> &#8212; \u0432\u0432\u043e\u0434\u043d\u0430\u044f \u0441\u0442\u0430\u0442\u044c\u044f \u043e \u0433\u0435\u043d\u0435\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0430\u0445 \u0438 \u043e\u0431 \u0438\u0445 \u0432\u0437\u0430\u0438\u043c\u043e\u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0438 \u0441 NLP \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0430\u043c\u0438.<\/p>\n<p><a href=\"https:\/\/machinelearningmastery.com\/differential-evolution-from-scratch-in-python\/\" rel=\"noopener noreferrer nofollow\"><strong><u>Differential Evolution from Scratch in Python<\/u><\/strong><\/a> &#8212; \u0441\u0442\u0430\u0442\u044c\u044f \u043e \u043c\u0435\u0442\u043e\u0434\u0430\u0445 \u043f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u044f \u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u043e\u0432 \u0434\u0438\u0444\u0444\u0435\u0440\u0435\u043d\u0446\u0438\u0430\u043b\u044c\u043d\u043e\u0439 \u044d\u0432\u043e\u043b\u044e\u0446\u0438\u0438 \u043d\u0430 Python.<\/p>\n<p><a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2021\/06\/style-your-pandas-dataframe-and-make-it-stunning\/\" rel=\"noopener noreferrer nofollow\"><strong><u>Style Your Pandas DataFrame and Make It Stunning<\/u><\/strong><\/a> &#8212; \u0442\u0435\u0445\u043d\u0438\u043a\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u0432\u0441\u0442\u0440\u043e\u0435\u043d\u043d\u044b\u0445 \u043c\u0435\u0442\u043e\u0434\u043e\u0432 \u0440\u0430\u0431\u043e\u0442\u044b \u0441 \u0434\u0430\u0442\u0430\u0444\u0440\u0435\u0439\u043c\u0430\u043c\u0438 \u043d\u0430 Pandas.<\/p>\n<p><a href=\"https:\/\/ai.facebook.com\/blog\/the-flores-101-data-set-helping-build-better-translation-systems-around-the-world\/\" rel=\"noopener noreferrer nofollow\"><strong><u>The FLORES-101 Data Set: Helping Build Better Translation Systems Around the World<\/u><\/strong><\/a> &#8212; \u043e\u0431\u0437\u043e\u0440 FLORES-101, \u043d\u043e\u0432\u043e\u0433\u043e \u043c\u0443\u043b\u044c\u0442\u0438\u0443\u0440\u043e\u0432\u043d\u0435\u0432\u043e\u0433\u043e \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u0430 \u0434\u043b\u044f \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u0431\u043e\u043b\u0435\u0435 \u0441\u043e\u0432\u0435\u0440\u0448\u0435\u043d\u043d\u044b\u0445 \u0441\u0438\u0441\u0442\u0435\u043c \u043f\u0435\u0440\u0435\u0432\u043e\u0434\u0430 \u043e\u0442 \u043a\u043e\u043c\u0430\u043d\u0434\u044b Facebook AI.<\/p>\n<h3>\u041d\u0430\u0443\u0447\u043d\u044b\u0435 \u0441\u0442\u0430\u0442\u044c\u0438<\/h3>\n<p><a href=\"https:\/\/nvlabs.github.io\/alias-free-gan\/\" rel=\"noopener noreferrer nofollow\"><strong><u>Alias-Free GAN<\/u><\/strong><\/a> &#8212; \u043d\u043e\u0432\u043e\u0435 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u0435 \u043e \u043f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u0438 \u043f\u0440\u043e\u0434\u0432\u0438\u043d\u0443\u0442\u044b\u0445 \u0433\u0435\u043d\u0435\u0440\u0430\u0442\u0438\u0432\u043d\u044b\u0445 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0434\u043b\u044f \u0440\u0430\u0431\u043e\u0442\u044b \u0441 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f\u043c\u0438, \u0432\u0438\u0434\u0435\u043e \u0438 \u0430\u043d\u0438\u043c\u0430\u0446\u0438\u0435\u0439 \u043e\u0442 NVIDIA.<\/p>\n<p><a href=\"https:\/\/arxiv.org\/abs\/2106.00666\" rel=\"noopener noreferrer nofollow\"><strong><u>You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection<\/u><\/strong><\/a> &#8212; \u0440\u0430\u0431\u043e\u0442\u0430 \u043e\u0442 \u043a\u0438\u0442\u0430\u0439\u0441\u043a\u0438\u0445 \u0443\u0447\u0435\u043d\u044b\u0445 \u043e\u0431 You Only Look at One Sequence (YOLOS), \u043d\u043e\u0432\u043e\u043c \u043d\u0430\u0431\u043e\u0440\u0435 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0434\u043b\u044f \u0434\u0435\u0442\u0435\u043a\u0446\u0438\u0438 \u043f\u0440\u0435\u0434\u043c\u0435\u0442\u043e\u0432.<\/p>\n<p><a href=\"https:\/\/arxiv.org\/abs\/2106.04803\" rel=\"noopener noreferrer nofollow\"><strong><u>CoAtNet: Marrying Convolution and Attention for All Data Sizes<\/u><\/strong><\/a> &#8212; \u043d\u0430\u0443\u0447\u043d\u0430\u044f \u0440\u0430\u0431\u043e\u0442\u0430 \u043e CoAtNets, \u043d\u043e\u0432\u043e\u043c \u0441\u0435\u043c\u0435\u0439\u0441\u0442\u0432\u0435 \u0433\u0438\u0431\u0440\u0438\u0434\u043d\u044b\u0445 \u0441\u0432\u0435\u0440\u0442\u043e\u0447\u043d\u044b\u0445 \u043c\u043e\u0434\u0435\u043b\u0435\u0439.<\/p>\n<p><a href=\"https:\/\/arxiv.org\/abs\/2106.05519\" rel=\"noopener noreferrer nofollow\"><strong><u>Consistent Instance False Positive Improves Fairness in Face Recognition<\/u><\/strong><\/a> &#8212; \u043e\u0431\u0437\u043e\u0440 \u043d\u043e\u0432\u043e\u0433\u043e \u043c\u0435\u0442\u043e\u0434\u0430 \u0443\u043c\u0435\u043d\u044c\u0448\u0435\u043d\u0438\u044f \u043b\u043e\u0436\u043d\u044b\u0445 \u0441\u0440\u0430\u0431\u0430\u0442\u044b\u0432\u0430\u043d\u0438\u0439 \u043f\u0440\u0438 \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u0438 \u043b\u0438\u0446.<\/p>\n<p><a href=\"https:\/\/arxiv.org\/abs\/2106.03823\" rel=\"noopener noreferrer nofollow\"><strong><u>Multivariate Probabilistic Regression with Natural Gradient Boosting<\/u><\/strong><\/a> &#8212; \u043d\u043e\u0432\u044b\u0439 \u044d\u0444\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u044b\u0439 \u043c\u0435\u0442\u043e\u0434 \u043c\u043d\u043e\u0433\u043e\u043c\u0435\u0440\u043d\u043e\u0439 \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u043d\u043e\u0439 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 \u0441 \u0435\u0441\u0442\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u044b\u043c \u0443\u0441\u0438\u043b\u0435\u043d\u0438\u0435\u043c \u0433\u0440\u0430\u0434\u0438\u0435\u043d\u0442\u0430.<\/p>\n<p><a href=\"https:\/\/dynamicvit.ivg-research.xyz\/\" rel=\"noopener noreferrer nofollow\"><strong><u>DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification<\/u><\/strong><\/a> &#8212; \u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a \u0434\u0438\u043d\u0430\u043c\u0438\u0447\u0435\u0441\u043a\u043e\u0433\u043e \u0440\u0430\u0437\u0431\u0438\u0435\u043d\u0438\u044f \u0442\u043e\u043a\u0435\u043d\u043e\u0432 \u0434\u043b\u044f \u043f\u043e\u0441\u0442\u0435\u043f\u0435\u043d\u043d\u043e\u0433\u043e \u0438 \u0434\u0438\u043d\u0430\u043c\u0438\u0447\u0435\u0441\u043a\u043e\u0433\u043e \u0443\u0434\u0430\u043b\u0435\u043d\u0438\u044f \u0438\u0437\u0431\u044b\u0442\u043e\u0447\u043d\u044b\u0445 \u0442\u043e\u043a\u0435\u043d\u043e\u0432 \u0432 \u0437\u0430\u0432\u0438\u0441\u0438\u043c\u043e\u0441\u0442\u0438 \u043e\u0442 \u0432\u0445\u043e\u0434\u043d\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445.<\/p>\n<h2>\u041f\u0440\u043e\u0435\u043a\u0442\u044b<\/h2>\n<p><a href=\"https:\/\/mlops.toys\/\" rel=\"noopener noreferrer nofollow\"><strong><u>MLOps Toys<\/u><\/strong><\/a> &#8212; \u043a\u0430\u0442\u0430\u043b\u043e\u0433 MLOps \u0442\u0443\u043b\u0437\u043e\u0432 \u0441 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u044c\u044e \u043f\u043e\u0438\u0441\u043a\u0430 \u043f\u043e \u043a\u0430\u0442\u0435\u0433\u043e\u0440\u0438\u044f\u043c.<\/p>\n<h3>\u0412\u0438\u0434\u0435\u043e<\/h3>\n<p><a href=\"https:\/\/www.youtube.com\/watch?v=tJ3v8h7A7RY\" rel=\"noopener noreferrer nofollow\"><strong><u>Data Governance<\/u><\/strong><\/a> &#8212; \u043f\u0440\u0435\u0434\u043c\u0435\u0442\u043d\u044b\u0439 \u0440\u0430\u0437\u0433\u043e\u0432\u043e\u0440 \u043e\u0431 \u0443\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0438 \u0434\u0430\u043d\u043d\u044b\u043c\u0438.<\/p>\n<p><a href=\"https:\/\/www.youtube.com\/watch?v=tZfN-8G0Yi0\" rel=\"noopener noreferrer nofollow\"><strong><u>Ingestion and Historization in the Data Lake<\/u><\/strong><\/a> &#8212; \u0434\u043e\u043a\u043b\u0430\u0434 \u043e \u043f\u0440\u0438\u0435\u043c\u0435 \u0438 \u0438\u0441\u0442\u043e\u0440\u0438\u0437\u0430\u0446\u0438\u0438 \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 \u043e\u0437\u0435\u0440\u0430\u0445 \u0434\u0430\u043d\u043d\u044b\u0445.<\/p>\n<hr>\n<p>\u0421\u043f\u0430\u0441\u0438\u0431\u043e, \u0447\u0442\u043e \u0434\u043e\u0447\u0438\u0442\u0430\u043b\u0438 \u044d\u0442\u043e\u0442 \u0432\u044b\u043f\u0443\u0441\u043a. \u041d\u0430\u0434\u0435\u044e\u0441\u044c, \u043a\u0430\u0436\u0434\u044b\u0439 \u043d\u0430\u0448\u0435\u043b \u0434\u043b\u044f \u0441\u0435\u0431\u044f \u0447\u0442\u043e-\u0442\u043e \u043f\u043e\u043b\u0435\u0437\u043d\u043e\u0435. \u0411\u0443\u0434\u0443 \u0431\u043b\u0430\u0433\u043e\u0434\u0430\u0440\u0435\u043d \u0437\u0430 \u043b\u044e\u0431\u044b\u0435 \u043f\u0440\u0435\u0434\u043b\u043e\u0436\u0435\u043d\u0438\u044f \u0434\u043b\u044f \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u0433\u043e \u0434\u0430\u0439\u0434\u0436\u0435\u0441\u0442\u0430.<\/p>\n<p>\u041f\u0440\u0438\u0441\u043e\u0435\u0434\u0438\u043d\u044f\u0439\u0442\u0435\u0441\u044c \u043a&nbsp;<a href=\"https:\/\/t.me\/DataPhoenix\" rel=\"noopener noreferrer nofollow\"><u>Telegram-\u043a\u0430\u043d\u0430\u043b\u0443<\/u><\/a>&nbsp;\u0434\u0430\u0439\u0434\u0436\u0435\u0441\u0442\u0430 \u0438 \u0435\u0433\u043e \u0441\u0442\u0440\u0430\u043d\u0438\u0446\u0430\u043c \u0432 \u0441\u043e\u0446\u0441\u0435\u0442\u044f\u0445: <a href=\"https:\/\/twitter.com\/Data_Phoenix\" rel=\"noopener noreferrer nofollow\"><u>Twitter<\/u><\/a>,&nbsp;<a href=\"https:\/\/www.facebook.com\/DataPhoenix.info\" rel=\"noopener noreferrer nofollow\"><u>Facebook<\/u><\/a>, \u0430 \u0442\u0430\u043a\u0436\u0435 \u043f\u043e\u0434\u043f\u0438\u0441\u044b\u0432\u0430\u0439\u0442\u0435\u0441\u044c \u043d\u0430 \u043d\u0430\u0448\u0443 \u0435\u0436\u0435\u043d\u0435\u0434\u0435\u043b\u044c\u043d\u0443\u044e <a href=\"https:\/\/dataphoenix.info\/subscribe\/\" rel=\"noopener noreferrer nofollow\">\u0440\u0430\u0441\u0441\u044b\u043b\u043a\u0443<\/a>.<\/p>\n<p>\u2190 <a href=\"https:\/\/habr.com\/ru\/post\/564484\/\" rel=\"noopener noreferrer nofollow\">\u041f\u0440\u0435\u0434\u044b\u0434\u0443\u0449\u0438\u0439 \u0432\u044b\u043f\u0443\u0441\u043a<\/a>.<\/p>\n<\/div>\n<p> \u0441\u0441\u044b\u043b\u043a\u0430 \u043d\u0430 \u043e\u0440\u0438\u0433\u0438\u043d\u0430\u043b \u0441\u0442\u0430\u0442\u044c\u0438 <a href=\"https:\/\/habr.com\/ru\/post\/565722\/\"> https:\/\/habr.com\/ru\/post\/565722\/<\/a><br \/><\/br><\/br><\/hr>\n<\/div>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-325807","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/posts\/325807","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=325807"}],"version-history":[{"count":0,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/posts\/325807\/revisions"}],"wp:attachment":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=325807"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=325807"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=325807"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}