{"id":115351,"date":"2025-12-16T13:56:39","date_gmt":"2025-12-16T13:56:39","guid":{"rendered":"https:\/\/bestsoln.com\/web\/?page_id=115351"},"modified":"2025-12-18T21:36:09","modified_gmt":"2025-12-18T21:36:09","slug":"introducing-transformers","status":"publish","type":"page","link":"https:\/\/bestsoln.com\/web\/courses\/fundamentals-of-ai-machine-learning-and-autonomous-agents\/introducing-transformers\/","title":{"rendered":"E. Introducing Transformers: The Architecture Powering Modern AI"},"content":{"rendered":"\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\t\t\t<!-- Flexy Breadcrumb -->\r\n\t\t\t<div class=\"fbc fbc-page\">\r\n\r\n\t\t\t\t<!-- Breadcrumb wrapper -->\r\n\t\t\t\t<div class=\"fbc-wrap\">\r\n\r\n\t\t\t\t\t<!-- Ordered list-->\r\n\t\t\t\t\t<ol class=\"fbc-items\" itemscope itemtype=\"https:\/\/schema.org\/BreadcrumbList\">\r\n\t\t\t\t\t\t            <li itemprop=\"itemListElement\" itemscope itemtype=\"https:\/\/schema.org\/ListItem\">\r\n                <span itemprop=\"name\">\r\n                    <!-- Home Link -->\r\n                    <a itemprop=\"item\" href=\"https:\/\/bestsoln.com\/web\">\r\n                    \r\n                                                    <i class=\"fa fa-home\" aria-hidden=\"true\"><\/i>Home                    <\/a>\r\n                <\/span>\r\n                <meta itemprop=\"position\" content=\"1\" \/><!-- Meta Position-->\r\n             <\/li><li><span class=\"fbc-separator\">\/<\/span><\/li><li class=\"active\" itemprop=\"itemListElement\" itemscope itemtype=\"https:\/\/schema.org\/ListItem\"><span itemprop=\"name\" title=\"E. Introducing Transformers: The Architecture Powering Modern AI\">E. Introducing Transformers: The Architecture...<\/span><meta itemprop=\"position\" content=\"2\" \/><\/li>\t\t\t\t\t<\/ol>\r\n\t\t\t\t\t<div class=\"clearfix\"><\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\t\t\t\n\n\n\n<p><\/p>\n<\/div>\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 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href=\"https:\/\/bestsoln.com\/web\/courses\/fundamentals-of-ai-machine-learning-and-autonomous-agents\/introducing-transformers\/#The_Architectural_Leap_From_Recurrent_to_Parallel\">The Architectural Leap: From Recurrent to Parallel<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/bestsoln.com\/web\/courses\/fundamentals-of-ai-machine-learning-and-autonomous-agents\/introducing-transformers\/#Self-Attention_Focus_and_Context\">Self-Attention: Focus and Context<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/bestsoln.com\/web\/courses\/fundamentals-of-ai-machine-learning-and-autonomous-agents\/introducing-transformers\/#Transfer_Learning_and_Model_Adaptation\">Transfer Learning and Model Adaptation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/bestsoln.com\/web\/courses\/fundamentals-of-ai-machine-learning-and-autonomous-agents\/introducing-transformers\/#Architecture_Variants_Encoder_versus_Decoder\">Architecture Variants: Encoder versus Decoder<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/bestsoln.com\/web\/courses\/fundamentals-of-ai-machine-learning-and-autonomous-agents\/introducing-transformers\/#Recommended_Readings\">Recommended Readings<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/bestsoln.com\/web\/courses\/fundamentals-of-ai-machine-learning-and-autonomous-agents\/introducing-transformers\/#FAQs\">FAQs<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/bestsoln.com\/web\/courses\/fundamentals-of-ai-machine-learning-and-autonomous-agents\/introducing-transformers\/#Conclusion\">Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n\n\n\n\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-buttons has-custom-font-size has-small-font-size is-content-justification-left is-layout-flex wp-container-core-buttons-is-layout-fc4fd283 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link has-white-color has-text-color has-background has-link-color wp-element-button\" href=\"https:\/\/t.me\/bestsoln\" style=\"border-radius:5px;background-color:#0088cc\" target=\"_blank\" rel=\"noreferrer noopener\">Join Telegram Channel<\/a><\/div>\n\n\n\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link has-white-color has-text-color has-background has-link-color wp-element-button\" href=\"https:\/\/whatsapp.com\/channel\/0029VaQv10P1NCrL6qZa0m13\" style=\"border-radius:5px;background-color:#25d366\" target=\"_blank\" rel=\"noreferrer noopener\">Join WhatsApp Channel<\/a><\/div>\n<\/div>\n\n\n\n<p><\/p>\n<\/div>\n\n\n\n<figure class=\"wp-block-embed is-type-rich is-provider-embed-handler wp-block-embed-embed-handler\"><div class=\"wp-block-embed__wrapper\">\n<audio class=\"wp-audio-shortcode\" id=\"audio-115351-2\" preload=\"none\" style=\"width: 100%;\" controls=\"controls\"><source type=\"audio\/mpeg\" src=\"https:\/\/bestsoln.com\/web\/wp-content\/uploads\/2025\/12\/Transformer-Parallel-Processing-and-Context.mp3?_=2\" \/><a href=\"https:\/\/bestsoln.com\/web\/wp-content\/uploads\/2025\/12\/Transformer-Parallel-Processing-and-Context.mp3\">https:\/\/bestsoln.com\/web\/wp-content\/uploads\/2025\/12\/Transformer-Parallel-Processing-and-Context.mp3<\/a><\/audio>\n<\/div><\/figure>\n\n\n\n<div class=\"wp-block-columns jusfy is-layout-flex wp-container-core-columns-is-layout-28f84493 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:15%\">\n<p>\u23f1\ufe0f Read Time:<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:66.66%\"><div class=\"wp-block-post-time-to-read\">5\u20138 minutes<\/div><\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading jusfy\"><span class=\"ez-toc-section\" id=\"Introduction\"><\/span>Introduction<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"jusfy\">The transition from classical Deep Learning (<a href=\"https:\/\/bestsoln.com\/web\/courses\/fundamentals-of-ai-machine-learning-and-autonomous-agents\/neural-networks\/\">Chapter 4<\/a>) to the era of Generative AI (Gen AI) was not gradual; it was immediate and explosive, triggered by a single architectural innovation: the <strong><a href=\"https:\/\/en.wikipedia.org\/wiki\/Transformer_(deep_learning)\" target=\"_blank\" rel=\"noreferrer noopener\">Transformer<\/a><\/strong>.<\/p>\n\n\n\n<p class=\"jusfy\">Prior systems, specifically <a href=\"https:\/\/en.wikipedia.org\/wiki\/Recurrent_neural_network\" target=\"_blank\" rel=\"noreferrer noopener\">Recurrent Neural Networks (RNNs)<\/a> and <a href=\"https:\/\/en.wikipedia.org\/wiki\/Long_short-term_memory\" target=\"_blank\" rel=\"noreferrer noopener\">Long Short-Term Memory (LSTMs)<\/a>, were the workhorses of sequence modeling (text, time series) for decades. However, they were fundamentally limited by their reliance on <strong>sequential processing<\/strong>, meaning they had to read and compute information one token, or one word, at a time. This inefficiency made them slow to train on massive modern datasets and incapable of easily maintaining context across very long sequences.<\/p>\n\n\n\n<p class=\"jusfy\">The Transformer model, introduced in a 2017 paper titled <a href=\"https:\/\/papers.neurips.cc\/paper\/7181-attention-is-all-you-need.pdf?utm_source=bestsoln.com\" target=\"_blank\" rel=\"noreferrer noopener\">&#8220;Attention Is All You Need,&#8221;<\/a> discarded this sequential bottleneck. By enabling parallel processing and introducing the novel <strong><a href=\"https:\/\/en.wikipedia.org\/wiki\/Attention_(machine_learning)\" target=\"_blank\" rel=\"noreferrer noopener\">Self-Attention Mechanism<\/a><\/strong>, the Transformer became the foundational technology for <a href=\"https:\/\/en.wikipedia.org\/wiki\/Large_language_model\" target=\"_blank\" rel=\"noreferrer noopener\">Large Language Models (LLMs)<\/a> and catalyzed the current wave of technological transformation.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading jusfy\"><span class=\"ez-toc-section\" id=\"The_Architectural_Leap_From_Recurrent_to_Parallel\"><\/span>The Architectural Leap: From Recurrent to Parallel<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"jusfy\">The central advantage of the Transformer architecture lies in its structure, which allows it to process an entire input sequence simultaneously rather than iteratively. This capacity for <strong>parallel processing<\/strong> allowed researchers to scale models exponentially in size, into the billions and trillions of parameters, using modern hardware like GPUs, a feat impossible with previous architectures.<\/p>\n\n\n\n<p class=\"jusfy\">The key distinction is simple yet profound:<\/p>\n\n\n\n<ul class=\"wp-block-list jusfy\">\n<li><strong>RNN\/LSTM:<\/strong> Must read, process, and retain memory of &#8220;The cat sat on the mat.&#8221; by processing one word, then the next, then the next, sequentially.<\/li>\n\n\n\n<li><strong>Transformer:<\/strong> Reads &#8220;The cat sat on the mat&#8221; all at once, calculating the relationship between <em>every word pair<\/em> simultaneously.<\/li>\n<\/ul>\n\n\n\n<p class=\"jusfy\">This change resolved the critical problem of <strong><a href=\"https:\/\/en.wikipedia.org\/wiki\/Long-range_dependence\" target=\"_blank\" rel=\"noreferrer noopener\">long-range dependencies<\/a><\/strong>, allowing the model to flawlessly connect a word at the beginning of a document with a relevant concept hundreds of words later, a task that frequently caused older RNNs to &#8220;forget&#8221; the initial context.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading jusfy\"><span class=\"ez-toc-section\" id=\"Self-Attention_Focus_and_Context\"><\/span>Self-Attention: Focus and Context<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"jusfy\">The core innovation that grants the Transformer its superior contextual understanding is the <strong>Self-Attention Mechanism<\/strong>. This mechanism is essentially a structured way for every element (<strong>token<\/strong>) in the input sequence to communicate with and weigh the importance of every other token in the same sequence.<\/p>\n\n\n\n<p class=\"jusfy\">This is often conceptualized using three vectors associated with each token:<\/p>\n\n\n\n<ol class=\"wp-block-list jusfy\">\n<li><strong>Query (Q):<\/strong> Represents the token asking a question. For the word &#8220;it&#8221; in a sentence, the query asks: &#8220;What other words in this sentence define &#8216;it&#8217;?&#8221;<\/li>\n\n\n\n<li><strong>Key (K):<\/strong> Represents the token answering the question. Every word acts as a key, offering its information to the queries.<\/li>\n\n\n\n<li><strong>Value (V):<\/strong> Represents the content payload to be retrieved. Once a query-key pair shows a strong match, the associated value is passed back, allowing the word &#8220;it&#8221; to pull in the contextual information of the word it refers to (e.g., &#8220;The river overflowed; it was wide&#8221;).<\/li>\n<\/ol>\n\n\n\n<p class=\"jusfy\">By calculating the mathematical similarity between every Query and every Key, the model generates <strong><a href=\"https:\/\/www.sciencedirect.com\/topics\/computer-science\/attention-score?utm_source=bestsoln.com\" target=\"_blank\" rel=\"noreferrer noopener\">Attention Scores<\/a><\/strong>. These scores are then normalized and used to create a weighted sum of all the Value vectors. This weighted sum becomes the new, contextually enriched representation of the original token. This dynamic ability to capture long-range dependencies across an entire sequence simultaneously is what gives modern LLMs their coherence and deep contextual understanding.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading jusfy\"><span class=\"ez-toc-section\" id=\"Transfer_Learning_and_Model_Adaptation\"><\/span>Transfer Learning and Model Adaptation<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"jusfy\">The creation of state-of-the-art Generative AI models relies heavily on <strong>Transfer Learning<\/strong>, a highly efficient training methodology facilitated by the Transformer architecture. This methodology consists of two core phases:<\/p>\n\n\n\n<ol class=\"wp-block-list jusfy\">\n<li><strong>Pretraining:<\/strong> This initial, computationally intensive phase involves training a massive, general-purpose model on colossal, diverse datasets sourced from the entire web, books, and code repositories. The model learns fundamental grammar, common facts, basic reasoning skills, and how language is structured. The goal is to acquire generalized knowledge (e.g., world facts, grammar).<\/li>\n\n\n\n<li><strong>Fine-tuning:<\/strong> Once pretrained, the large model is too general for specific tasks. <strong>Fine-tuning<\/strong> takes the pretrained model and subjects it to specialized, smaller, and highly focused labeled datasets to adapt it for a particular application (e.g., training a general LLM to become an expert legal assistant or a specialized code generator).<\/li>\n<\/ol>\n\n\n\n<p class=\"jusfy\">This two-step process allows generalized knowledge to be efficiently specialized and repurposed, a key factor in the rapid and widespread deployment of LLMs across various industries.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading jusfy\"><span class=\"ez-toc-section\" id=\"Architecture_Variants_Encoder_versus_Decoder\"><\/span>Architecture Variants: Encoder versus Decoder<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"jusfy\">While all modern large language models use the Transformer architecture, they are often implemented with distinct components depending on their intended task:<\/p>\n\n\n\n<ul class=\"wp-block-list jusfy\">\n<li><strong><a href=\"https:\/\/medium.com\/@pickleprat\/encoder-only-architecture-bert-4b27f9c76860?utm_source=bestsoln.com\" target=\"_blank\" rel=\"noreferrer noopener\">Encoder-Only Models (e.g., BERT)<\/a>:<\/strong> The Encoder focuses on <strong>representation<\/strong> and <strong>contextual understanding<\/strong>. It processes the entire input sequence and creates a rich, bidirectional contextual embedding of every token. Encoder-only models are excellent for tasks like sentiment analysis, semantic search, and sentence classification, where the goal is to understand the input thoroughly.<\/li>\n\n\n\n<li><strong><a href=\"https:\/\/cameronrwolfe.substack.com\/p\/decoder-only-transformers-the-workhorse?utm_source=bestsoln.com\" target=\"_blank\" rel=\"noreferrer noopener\">Decoder-Only Models (e.g., GPT family)<\/a>:<\/strong> The Decoder is <strong>autoregressive<\/strong>, meaning it is designed to predict the next token in a sequence based only on the tokens that preceded it. This unidirectional focus is ideal for <strong>Generative AI<\/strong>, where the goal is continuous, human-like creation of text, code, or novel sequences.<\/li>\n<\/ul>\n\n\n\n<p class=\"jusfy\">It is crucial to maintain a precise distinction in terminology: the <strong>Transformer<\/strong> is the specific algorithm or architecture, while a <strong>Large Language Model (LLM)<\/strong> is the application, the massive model whose primary goal is to perform <strong>next-token prediction<\/strong> to generate content.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading jusfy\"><span class=\"ez-toc-section\" id=\"Recommended_Readings\"><\/span>Recommended Readings<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<ul class=\"wp-block-list jusfy\">\n<li><strong><a href=\"https:\/\/bestsoln.com\/shortener\/redirect.php?code=c20601\" target=\"_blank\" rel=\"noreferrer noopener\">\u201cSupremacy: AI, ChatGPT, and the Race that Will Change the World\u201d<\/a> by Parmy Olson<\/strong> &#8211; A timely analysis of the technological and corporate race to dominate the Generative AI landscape.<\/li>\n\n\n\n<li><strong><a href=\"https:\/\/bestsoln.com\/shortener\/redirect.php?code=11850c\" target=\"_blank\" rel=\"noreferrer noopener\">\u201cDeep Learning\u201d<\/a> by Ian Goodfellow, Yoshua Bengio, and Aaron Courville<\/strong> &#8211; Provides the fundamental mathematical and conceptual underpinning for understanding the mechanics of attention mechanisms.<\/li>\n\n\n\n<li><strong><a href=\"https:\/\/bestsoln.com\/shortener\/redirect.php?code=07d937\" target=\"_blank\" rel=\"noreferrer noopener\">\u201cThe Singularity Is Nearer: When We Merge with AI\u201d<\/a> by Ray Kurzweil<\/strong> &#8211; Offers a provocative, forward-looking view on how these accelerating technologies will integrate with human life.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading jusfy\"><span class=\"ez-toc-section\" id=\"FAQs\"><\/span>FAQs<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"jusfy\"><strong>Q1: What is the key difference between a Transformer and a Recurrent Network (RNN)?<\/strong><\/p>\n\n\n\n<p class=\"jusfy\"><strong>A: <\/strong>RNNs process data sequentially, which makes them slow and prone to losing context over long sequences. The Transformer uses the Self-Attention mechanism to process the entire sequence in parallel, which is much faster and more effective at capturing long-range dependencies.<\/p>\n\n\n\n<p class=\"jusfy\"><strong>Q2:What is the distinction between a Large Language Model (LLM) and a Transformer?<\/strong><\/p>\n\n\n\n<p class=\"jusfy\"><strong>A: <\/strong>The Transformer is the underlying <strong>architecture or algorithm<\/strong> (the method used to predict the next token). The LLM is the <strong>application<\/strong>, a massive model trained on vast data whose goal is to use that architecture to predict the next token and generate content.<\/p>\n\n\n\n<p class=\"jusfy\"><strong>Q3: What is Transfer Learning in the context of LLMs?<\/strong><\/p>\n\n\n\n<p class=\"jusfy\"><strong>A: <\/strong>It is a two-step process: <strong>Pretraining<\/strong> the model on massive, generalized data (to learn foundational knowledge), followed by <strong>Fine-tuning<\/strong> it on smaller, specialized data to adapt it to a specific task or domain.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading jusfy\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span>Conclusion<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"jusfy\">The Transformer, enabled by the Self-Attention mechanism, fundamentally solved the scalability and context issues that constrained previous sequential architectures. This single breakthrough provided the foundation necessary to build the immense, highly coherent models that define the Generative AI revolution, enabling systems not just to analyze, but to create. Our next chapter will delve into the full scope of these Generative AI capabilities and the techniques required to interact with them effectively.<\/p>\n\n\n\n<div class=\"wp-block-columns is-not-stacked-on-mobile is-layout-flex wp-container-core-columns-is-layout-28f84493 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:35%\">\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link has-xx-small-font-size has-custom-font-size wp-element-button\" href=\"https:\/\/bestsoln.com\/web\/courses\/fundamentals-of-ai-machine-learning-and-autonomous-agents\/neural-networks\/\">&lt; Previous<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:30%\"><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" 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class=\"wp-block-social-link-label\">Instagram<\/span><\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This chapter unveils the Transformer architecture, which revolutionized AI with the Self-Attention Mechanism. This parallel-processing design captures Long-Range Dependencies, enabling massive scaling and Transfer Learning (Pretraining\/Fine-tuning). It is the foundation of all modern Generative AI and LLMs.<\/p>\n","protected":false},"author":1,"featured_media":115499,"parent":115241,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-with-right-sidebar","meta":{"googlesitekit_rrm_CAow1snDDA:productID":"","MSN_Categories":"Uncategorized","MSN_Publish_Option":false,"MSN_Is_Local_News":false,"MSN_Is_AIAC_Included":"Empty","MSN_Location":"[]","MSN_Add_Feature_Img_On_Top_Of_Post":false,"MSN_Has_Custom_Author":false,"MSN_Custom_Author":"","MSN_Has_Custom_Canonical_Url":false,"MSN_Custom_Canonical_Url":"","footnotes":""},"class_list":["post-115351","page","type-page","status-publish","has-post-thumbnail","hentry"],"_links":{"self":[{"href":"https:\/\/bestsoln.com\/web\/wp-json\/wp\/v2\/pages\/115351","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/bestsoln.com\/web\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/bestsoln.com\/web\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/bestsoln.com\/web\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/bestsoln.com\/web\/wp-json\/wp\/v2\/comments?post=115351"}],"version-history":[{"count":10,"href":"https:\/\/bestsoln.com\/web\/wp-json\/wp\/v2\/pages\/115351\/revisions"}],"predecessor-version":[{"id":115501,"href":"https:\/\/bestsoln.com\/web\/wp-json\/wp\/v2\/pages\/115351\/revisions\/115501"}],"up":[{"embeddable":true,"href":"https:\/\/bestsoln.com\/web\/wp-json\/wp\/v2\/pages\/115241"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/bestsoln.com\/web\/wp-json\/wp\/v2\/media\/115499"}],"wp:attachment":[{"href":"https:\/\/bestsoln.com\/web\/wp-json\/wp\/v2\/media?parent=115351"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}