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<oembed><version>1.0</version><provider_name>Business Physics AI Lab</provider_name><provider_url>https://businessphysics.ai/fr</provider_url><author_name>Hichem Benzair</author_name><author_url>https://businessphysics.ai/fr/author/hichem-benzair/</author_url><title>Understanding Transformer Architecture in Simple Terms - Business Physics AI Lab</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="roJmHrSdXB"&gt;&lt;a href="https://businessphysics.ai/fr/understanding-transformer-architecture-in-simple-terms/"&gt;Comprendre l'architecture des transformateurs en termes simples&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://businessphysics.ai/fr/understanding-transformer-architecture-in-simple-terms/embed/#?secret=roJmHrSdXB" width="600" height="338" title="&laquo; Understanding Transformer Architecture in Simple Terms &raquo; &#x2014; Business Physics AI Lab" data-secret="roJmHrSdXB" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script&gt;
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&lt;/script&gt;</html><description>What Are Transformers? Transformers are a type of neural network architecture named for their ability to &#x201C;transform&#x201D; how artificial intelligence (AI) processes sequences of data, especially text. Introduced by Google researchers in their 2017 paper, Attention Is All You Need, Transformers significantly improved Natural Language Processing (NLP) tasks by using a mechanism called Self-Attention (Golroudbari). [&hellip;]</description><thumbnail_url>https://businessphysics.ai/wp-content/uploads/2025/03/head-view.gif</thumbnail_url><thumbnail_width>566</thumbnail_width><thumbnail_height>498</thumbnail_height></oembed>
