{"id":31269,"date":"2025-02-28T21:50:03","date_gmt":"2025-02-28T19:50:03","guid":{"rendered":"https:\/\/rss.eground-zerkalo.com\/?p=31269"},"modified":"2025-02-28T21:50:03","modified_gmt":"2025-02-28T19:50:03","slug":"%d0%ba%d0%b2%d0%b0%d0%bd%d1%82%d0%be%d0%b2%d0%b0%d0%bd%d0%b8%d0%b5-%d0%b4%d0%bb%d1%8f-%d0%bc%d0%be%d0%b4%d0%b5%d0%bb%d0%b5%d0%b9-genai-udemy-start-tech-academy","status":"publish","type":"post","link":"https:\/\/rss.eground-zerkalo.com\/?p=31269","title":{"rendered":"\u041a\u0432\u0430\u043d\u0442\u043e\u0432\u0430\u043d\u0438\u0435 \u0434\u043b\u044f \u043c\u043e\u0434\u0435\u043b\u0435\u0439 GenAI [udemy] [Start-Tech Academy]"},"content":{"rendered":"<h2 class=\"\">\u0421\u043a\u043b\u0430\u0434\u0447\u0438\u043d\u0430: \u041a\u0432\u0430\u043d\u0442\u043e\u0432\u0430\u043d\u0438\u0435 \u0434\u043b\u044f \u043c\u043e\u0434\u0435\u043b\u0435\u0439 GenAI [udemy] [Start-Tech Academy]<\/h2>\n<p> \t\t\t\t\t<b>Quantization for GenAI Models<br \/> \u042f\u0437\u044b\u043a \u043a\u0443\u0440\u0441\u0430 \u0430\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 + \u043e\u0440\u0433 \u0441\u0434\u0435\u043b\u0430\u0435\u0442 \u0440\u0443\u0441\u0441\u043a\u0443\u044e \u0430\u0443\u0434\u0438\u043e\u0434\u043e\u0440\u043e\u0436\u043a\u0443 [\u0430\u0432\u0442\u043e]<br \/>  \t \t<img decoding=\"async\" src=\"https:\/\/v21.skladchik.org\/attachments\/kvantovanie-jpg.1105219\/\" class=\"bbCodeImage LbImage\" alt=\"\u043a\u0432\u0430\u043d\u0442\u043e\u0432\u0430\u043d\u0438\u0435.jpg\" \/> \t\t  <br \/> \u0427\u0435\u043c\u0443 \u0412\u044b \u043d\u0430\u0443\u0447\u0438\u0442\u0435\u0441\u044c:<\/b> <\/p>\n<ul>\n<li>\u041f\u043e\u043d\u0438\u043c\u0430\u0442\u044c \u043c\u0435\u0442\u043e\u0434\u044b \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u0438 \u043c\u043e\u0434\u0435\u043b\u0435\u0439: \u043e\u0431\u0440\u0435\u0437\u043a\u0430, \u0434\u0438\u0441\u0442\u0438\u043b\u043b\u044f\u0446\u0438\u044f \u0438 \u043a\u0432\u0430\u043d\u0442\u043e\u0432\u0430\u043d\u0438\u0435 <\/li>\n<li>\u0418\u0437\u0443\u0447\u0438\u0442\u0435 \u043e\u0441\u043d\u043e\u0432\u044b \u0442\u0438\u043f\u043e\u0432 \u0434\u0430\u043d\u043d\u044b\u0445, \u0442\u0430\u043a\u0438\u0445 \u043a\u0430\u043a FP32, FP16, BFloat16 \u0438 INT8 <\/li>\n<li>\u041e\u0441\u0432\u043e\u0438\u0442\u0435 \u043f\u043e\u043d\u0438\u0436\u0435\u043d\u0438\u0435 \u0442\u0438\u043f\u0430 FP32 \u0434\u043e BF16 \u0438 FP32 \u0434\u043e INT8 <\/li>\n<li>\u0418\u0437\u0443\u0447\u0438\u0442\u0435 \u0440\u0430\u0437\u043d\u0438\u0446\u0443 \u043c\u0435\u0436\u0434\u0443 \u0441\u0438\u043c\u043c\u0435\u0442\u0440\u0438\u0447\u043d\u044b\u043c \u0438 \u0430\u0441\u0438\u043c\u043c\u0435\u0442\u0440\u0438\u0447\u043d\u044b\u043c \u043a\u0432\u0430\u043d\u0442\u043e\u0432\u0430\u043d\u0438\u0435\u043c <\/li>\n<li>\u0420\u0435\u0430\u043b\u0438\u0437\u0443\u0435\u0442\u0435 \u043c\u0435\u0442\u043e\u0434\u044b \u043a\u0432\u0430\u043d\u0442\u043e\u0432\u0430\u043d\u0438\u044f \u0432 Python \u0441 \u0440\u0435\u0430\u043b\u044c\u043d\u044b\u043c\u0438 \u043f\u0440\u0438\u043c\u0435\u0440\u0430\u043c\u0438 <\/li>\n<li>\u041f\u0440\u0438\u043c\u0435\u043d\u0438\u0442\u0435 \u043a\u0432\u0430\u043d\u0442\u043e\u0432\u0430\u043d\u0438\u0435, \u0447\u0442\u043e\u0431\u044b \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u043c\u043e\u0434\u0435\u043b\u0438 \u0431\u043e\u043b\u0435\u0435 \u044d\u0444\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u044b\u043c\u0438 \u0438 \u0433\u043e\u0442\u043e\u0432\u044b\u043c\u0438 \u043a \u0440\u0430\u0437\u0432\u0435\u0440\u0442\u044b\u0432\u0430\u043d\u0438\u044e <\/li>\n<li>\u041f\u0440\u0438\u043e\u0431\u0440\u0435\u0442\u0438\u0442\u0435 \u043f\u0440\u0430\u043a\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u043d\u0430\u0432\u044b\u043a\u0438 \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u0438 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 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\u043a\u0443\u0440\u0441\u0430:<\/b><br \/> 5 \u0440\u0430\u0437\u0434\u0435\u043b\u043e\u0432 \u2022 24 \u043b\u0435\u043a\u0446\u0438\u0439 \u2022 \u041e\u0431\u0449\u0430\u044f \u043f\u0440\u043e\u0434\u043e\u043b\u0436\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c 2 \u0447 35 \u043c\u0438\u043d<br \/> 1.\u0412\u0432\u0435\u0434\u0435\u043d\u0438\u0435<br \/> 2.\u041c\u0435\u0442\u043e\u0434\u044b \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u0438 \u043c\u043e\u0434\u0435\u043b\u0438 Gen AI<br \/> 2.1.\u0412\u0432\u0435\u0434\u0435\u043d\u0438\u0435 \u0432 \u043c\u043e\u0434\u0435\u043b\u0438 Gen AI<br \/> 2.2.\u041c\u0435\u0442\u043e\u0434\u044b \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u0438 \u043c\u043e\u0434\u0435\u043b\u0438 &#8212; \u0432\u0432\u0435\u0434\u0435\u043d\u0438\u0435<br \/> 2.3.\u0412\u0432\u0435\u0434\u0435\u043d\u0438\u0435 \u0432 \u043e\u0431\u0440\u0435\u0437\u043a\u0443<br \/> 2.4.\u0412\u0432\u0435\u0434\u0435\u043d\u0438\u0435 \u0432 \u0434\u0438\u0441\u0442\u0438\u043b\u043b\u044f\u0446\u0438\u044e \u0437\u043d\u0430\u043d\u0438\u0439<br \/> 2.5.\u0412\u0432\u0435\u0434\u0435\u043d\u0438\u0435 \u0432 \u043a\u0432\u0430\u043d\u0442\u043e\u0432\u0430\u043d\u0438\u0435<br \/> 3.\u0422\u0438\u043f\u044b \u0434\u0430\u043d\u043d\u044b\u0445 \u0438 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u0447\u0438\u0441\u0435\u043b<br \/> 3.1.\u0422\u0438\u043f\u044b \u0434\u0430\u043d\u043d\u044b\u0445 \u0438 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u0447\u0438\u0441\u043b\u0430<br \/> 3.2.\u0426\u0435\u043b\u043e\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044b\u0435 \u0442\u0438\u043f\u044b \u0434\u0430\u043d\u043d\u044b\u0445<br \/> 3.3.\u0426\u0435\u043b\u043e\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u043e\u0435 \u0442\u0438\u043f\u0438\u043d \u0434\u0430\u043d\u043d\u044b\u0445 pytorch<br \/> 3.4.8-\u0431\u0438\u0442\u043d\u044b\u0435 \u043d\u043e\u043c\u0435\u0440\u0430 \u0441 \u0444\u0438\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u043e\u0439 \u0442\u043e\u0447\u043a\u043e\u0439<br \/> 3.5.\u041d\u043e\u043c\u0435\u0440\u0430 \u0441 \u043f\u043b\u0430\u0432\u0430\u044e\u0449\u0435\u0439 \u0437\u0430\u043f\u044f\u0442\u043e\u0439<br \/> 3.6.\u0414\u0440\u0443\u0433\u0438\u0435 \u0444\u043e\u0440\u043c\u0430\u0442\u044b \u0441 \u043f\u043b\u0430\u0432\u0430\u044e\u0449\u0435\u0439 \u0442\u043e\u0447\u043a\u043e\u0439<br \/> 3.7.\u0422\u0438\u043f\u044b \u0434\u0430\u043d\u043d\u044b\u0445 \u0441 \u043f\u043b\u0430\u0432\u0430\u044e\u0449\u0435\u0439 \u0437\u0430\u043f\u044f\u0442\u043e\u0439 \u0432 Pytorch<br \/> 3.8.\u0414\u0440\u0443\u0433\u0438\u0435 \u0444\u043e\u0440\u043c\u0430\u0442\u044b<br \/> 4.\u041a\u0432\u0430\u043d\u0442\u043e\u0432\u0430\u043d\u0438\u0435<br \/> 4.1.\u0423\u043d\u0438\u0447\u0442\u043e\u0436\u0435\u043d\u0438\u0435 FP32 \u0434\u043e BF16<br \/> 4.2.\u041f\u043e\u043d\u0438\u0436\u0435\u043d\u0438\u0435 \u0442\u0435\u043d\u0437\u043e\u0440\u043e\u0432 \u0432 \u043f\u0438\u0442\u043e\u043d\u0435<br \/> 4.3.\u041f\u043e\u043d\u0438\u0436\u0435\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 ML \u0432 Python<br \/> 4.4.\u0423\u043d\u0438\u0447\u0442\u043e\u0436\u0435\u043d\u0438\u0435 FP32 \u0434\u043e Int8<br \/> 4.5.\u041a\u0432\u0430\u043d\u0442\u043e\u0432\u0430\u043d\u0438\u0435 \u0441\u0438\u043c\u043c\u0435\u0442\u0440\u0438\u043a\u0438<br \/> 4.6.\u041a\u0432\u0430\u043d\u0442\u043e\u0432\u0430\u043d\u0438\u0435 \u0430\u0441\u0438\u043c\u043c\u0435\u0442\u0440\u0438\u043a\u0438<br \/> 4.7.Gpt neo 125 \u043a\u0432\u0430\u043d\u0442\u043e\u0432\u0430\u043d\u0438\u0435<br \/> 5.\u0417\u0430\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435<br \/> \u041e\u0440\u0438\u0433\u0438\u043d\u0430\u043b:<br \/>     \t\u0421\u043f\u043e\u0439\u043b\u0435\u0440 \tIf you are a developer, data scientist, or machine learning enthusiast who wants to optimize and deploy efficient AI models, this course is for you. Do you want to make your models faster and more resource-efficient while maintaining performance? Are you looking to learn how to apply quantization techniques for better model deployment? This course will teach you how to implement practical quantization techniques, making your models lean and deployable on edge devices.<br \/> <b>In this course, you will:<\/b><br \/> Learn the core concepts of <b>Quantization<\/b>, <b>Pruning<\/b>, and <b>Distillation<\/b>. <\/p>\n<ol>\n<li>Understand different <b>data types<\/b> like FP32, FP16, BFloat16, and INT8.<\/li>\n<li>Explore how to convert FP32 to BF16 and INT8 for efficient model compression.<\/li>\n<li>Implement <b>symmetric<\/b> and <b>asymmetric quantization<\/b> in Python with real-world applications.<\/li>\n<li>Understand how to downcast model parameters from FP32 to INT8 for deployment.<\/li>\n<li>Gain hands-on experience with Python-based quantization, making your models suitable for mobile and IoT devices<\/li>\n<\/ol>\n<p><b>Why learn quantization?<\/b> Quantization allows you to reduce the size and computational load of models, making them suitable for resource-constrained devices like smartphones, IoT devices, and embedded systems. By mastering quantization, you can ensure your models are faster, more energy-efficient, and easier to deploy while maintaining accuracy.<br \/> Throughout the course, you\u2019ll learn to implement quantization techniques and optimize your models for real-world applications. This course provides the <b>perfect balance of theory and practical application<\/b> for making machine learning models more efficient.<br \/> By the end of the course, you\u2019ll have a deep understanding of quantization, and the ability to optimize and deploy efficient models on edge devices.<br \/> Ready to optimize your AI models for efficiency and performance? Enroll now and start your journey \u0426\u0435\u043d\u0430: 19,99 \u20ac (\u043f\u0440\u0438\u043c\u0435\u0440\u043d\u043e 1830 \u0440\u0443\u0431.)<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u0421\u043a\u043b\u0430\u0434\u0447\u0438\u043d\u0430: \u041a\u0432\u0430\u043d\u0442\u043e\u0432\u0430\u043d\u0438\u0435 \u0434\u043b\u044f \u043c\u043e\u0434\u0435\u043b\u0435\u0439 GenAI [udemy] [Start-Tech Academy] Quantization for GenAI Models \u042f\u0437\u044b\u043a \u043a\u0443\u0440\u0441\u0430 \u0430\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 + \u043e\u0440\u0433 \u0441\u0434\u0435\u043b\u0430\u0435\u0442 \u0440\u0443\u0441\u0441\u043a\u0443\u044e \u0430\u0443\u0434\u0438\u043e\u0434\u043e\u0440\u043e\u0436\u043a\u0443 [\u0430\u0432\u0442\u043e] \u0427\u0435\u043c\u0443 \u0412\u044b \u043d\u0430\u0443\u0447\u0438\u0442\u0435\u0441\u044c: \u041f\u043e\u043d\u0438\u043c\u0430\u0442\u044c \u043c\u0435\u0442\u043e\u0434\u044b \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u0438 \u043c\u043e\u0434\u0435\u043b\u0435\u0439: \u043e\u0431\u0440\u0435\u0437\u043a\u0430, \u0434\u0438\u0441\u0442\u0438\u043b\u043b\u044f\u0446\u0438\u044f \u0438 \u043a\u0432\u0430\u043d\u0442\u043e\u0432\u0430\u043d\u0438\u0435 \u0418\u0437\u0443\u0447\u0438\u0442\u0435 \u043e\u0441\u043d\u043e\u0432\u044b \u0442\u0438\u043f\u043e\u0432 \u0434\u0430\u043d\u043d\u044b\u0445, \u0442\u0430\u043a\u0438\u0445 \u043a\u0430\u043a FP32, FP16, BFloat16 \u0438 INT8 \u041e\u0441\u0432\u043e\u0438\u0442\u0435 \u043f\u043e\u043d\u0438\u0436\u0435\u043d\u0438\u0435 \u0442\u0438\u043f\u0430 FP32 \u0434\u043e BF16 \u0438 FP32 \u0434\u043e INT8 \u0418\u0437\u0443\u0447\u0438\u0442\u0435 \u0440\u0430\u0437\u043d\u0438\u0446\u0443 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-31269","post","type-post","status-publish","format-standard","hentry","category-rss"],"_links":{"self":[{"href":"https:\/\/rss.eground-zerkalo.com\/index.php?rest_route=\/wp\/v2\/posts\/31269","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rss.eground-zerkalo.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/rss.eground-zerkalo.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/rss.eground-zerkalo.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/rss.eground-zerkalo.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=31269"}],"version-history":[{"count":0,"href":"https:\/\/rss.eground-zerkalo.com\/index.php?rest_route=\/wp\/v2\/posts\/31269\/revisions"}],"wp:attachment":[{"href":"https:\/\/rss.eground-zerkalo.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=31269"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/rss.eground-zerkalo.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=31269"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/rss.eground-zerkalo.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=31269"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}