{"id":37737,"date":"2025-02-03T00:32:36","date_gmt":"2025-02-03T05:32:36","guid":{"rendered":"https:\/\/statanalytica.com\/blog\/?p=37737"},"modified":"2025-02-03T00:32:40","modified_gmt":"2025-02-03T05:32:40","slug":"pytorch-for-deep-learning","status":"publish","type":"post","link":"https:\/\/statanalytica.com\/blog\/pytorch-for-deep-learning\/","title":{"rendered":"Unlocking the Power of PyTorch for Deep Learning and Data Analysis"},"content":{"rendered":"\n<p>Selecting a framework is very important for deep learning and data analysis \u2014 especially\u2002because many deep learning frameworks are still either new or changing rapidly. Recently, PyTorch has become one of the most\u2002usable frameworks among researchers and practitioners due to its flexibility, easy design, and functionality. This guide explains how you can use PyTorch for deep learning and data analysis and gives you everything you need to know to get the most out\u2002of PyTorch.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"introduction-to-pytorch\"><\/span><strong>Introduction to PyTorch<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2><div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-light-blue ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69f61db32b0ae\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #ff5104;color:#ff5104\" 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 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #ff5104;color:#ff5104\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69f61db32b0ae\" checked aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/statanalytica.com\/blog\/pytorch-for-deep-learning\/#introduction-to-pytorch\" >Introduction to PyTorch<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/statanalytica.com\/blog\/pytorch-for-deep-learning\/#why-choose-pytorch-for-deep-learning\" >Why Choose PyTorch for Deep Learning?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/statanalytica.com\/blog\/pytorch-for-deep-learning\/#getting-started-with-pytorch\" >Getting Started with PyTorch<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/statanalytica.com\/blog\/pytorch-for-deep-learning\/#installation\" >Installation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/statanalytica.com\/blog\/pytorch-for-deep-learning\/#understanding-tensors\" >Understanding Tensors<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/statanalytica.com\/blog\/pytorch-for-deep-learning\/#utilizing-gpu-acceleration\" >Utilizing GPU Acceleration<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/statanalytica.com\/blog\/pytorch-for-deep-learning\/#building-neural-networks-with-pytorch\" >Building Neural Networks with PyTorch<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/statanalytica.com\/blog\/pytorch-for-deep-learning\/#defining-a-neural-network\" >Defining a Neural Network<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/statanalytica.com\/blog\/pytorch-for-deep-learning\/#training-the-model\" >Training the Model<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/statanalytica.com\/blog\/pytorch-for-deep-learning\/#data-analysis-using-pytorch\" >Data Analysis Using PyTorch<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/statanalytica.com\/blog\/pytorch-for-deep-learning\/#loading-and-preprocessing-data\" >Loading and Preprocessing Data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/statanalytica.com\/blog\/pytorch-for-deep-learning\/#performing-data-analysis\" >Performing Data Analysis<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/statanalytica.com\/blog\/pytorch-for-deep-learning\/#conclusion\" >Conclusion<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/statanalytica.com\/blog\/pytorch-for-deep-learning\/#is-pytorch-better-than-tensorflow\" >Is PyTorch better than TensorFlow?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/statanalytica.com\/blog\/pytorch-for-deep-learning\/#can-pytorch-run-on-a-cpu\" >Can PyTorch run on a CPU?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/statanalytica.com\/blog\/pytorch-for-deep-learning\/#what-are-pytorch-tensors\" >What are PyTorch tensors?<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n\n\n\n\n<p>PyTorch is a free and open-source deep learning framework created by Facebook&#8217;s\u2002AI Research lab. Offering a highly flexible and intuitive platform to build and train neural networks, this is\u2002why both novices and professional data scientists very love it. Dynamic computation graphs set it apart from its users, whereas integration with\u2002Python programming makes it a unique experience in developing deep learning models.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"why-choose-pytorch-for-deep-learning\"><\/span><strong>Why Choose PyTorch for Deep Learning?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Several factors contribute to PyTorch&#8217;s popularity in the deep learning community:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Dynamic Computation Graphs:<\/strong> In contrast to static computation graphs, which we used in\u2002some frameworks, PyTorch dynamic graphs enable us to change how the neural network behaves using ease of debugging and experimentation.<\/li>\n\n\n\n<li><strong>Pythonic:<\/strong> The very nature of PyTorch design is built very close to Python, less challenging for Python developers\u2002to learn and easy to use code.<\/li>\n\n\n\n<li><strong>Rich Developer Community:<\/strong>\u2002The larger the community behind a framework, the more resources, tutorials, forums, and general assistance for developers to use to troubleshoot and stay abreast with new techniques.<\/li>\n\n\n\n<li><strong>Rich Ecosystem:<\/strong> PyTorch has a large and growing ecosystem of libraries (e.g., TorchVision for image processing, TorchText for NLP,\u2002and TorchAudio for audio processing, etc.) that provide standard datasets and pre-trained models, so you can shortcut your process by leveraging those building blocks to reduce the hand\u2013-waving\u2013-time (the process that comes before the science) across different domains.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"getting-started-with-pytorch\"><\/span><strong>Getting Started with PyTorch<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Embarking on your PyTorch journey involves a few essential steps:<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"installation\"><\/span><strong>Installation<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>PyTorch is available for installation via some package managers\u2002such as pip or conda. You have\u2002to choose the right one that suits your hardware and software configurations For the installation guide, please refer to\u2002the official PyTorch website.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"understanding-tensors\"><\/span><strong>Understanding Tensors<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>The primary building\u2002blocks of the most important library structure in PyTorch are tensors- multi-dimensional arrays. On the\u2002same model, they are like NumPy array but always with the feature of GPU acceleration to make computation more efficient.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"utilizing-gpu-acceleration\"><\/span><strong>Utilizing GPU Acceleration<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>Seamless GPU Integration: One of the main advantages of PyTorch is it allows you to run\u2002your work on GPU without any hassle. Transferring\u2002tensors and models \u2192 Using GPU can yield substantial performance relatively for large-scale neural networks.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXcmbK-Lj1hJFiHtab1pTBII5OzgzQdB2-KeWhdKGYgTVgzr6WPNQauTXKLPkqBQmr3B1zi6JIEiBjINDayaEJ-3f6P1K0pm--kocqNWqztt9f7hy4BOmqsYoXGUTgY_eQIeXXsnOw?key=zareTrb70yP6YEeJ_Thf2ocC\" alt=\"\"\/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"building-neural-networks-with-pytorch\"><\/span><strong>Building Neural Networks with PyTorch<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Constructing neural networks in PyTorch is both straightforward and flexible, thanks to its modular design.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"defining-a-neural-network\"><\/span><strong>Defining a Neural Network<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>In PyTorch, neural networks are defined by subclassing torch.nn.Module and specifying the network&#8217;s layers and forward pass.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXdsS2ca54Zr0P5StlczFf1XIjLbj6cD9ePi7131VRgMr7ojKFnXNT4TFrf7sP_pFJc2zMJd4Y4cTljxMl521dgzz9nHSWJTXWoniaT2RKWZIYZsU0yaLwkzwZ0xRnvKGJfX02OFDA?key=zareTrb70yP6YEeJ_Thf2ocC\" alt=\"\"\/><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"training-the-model\"><\/span><strong>Training the Model<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>Training involves defining a loss function and an optimizer, followed by iterating over the dataset to adjust the model&#8217;s parameters.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXdUMwvGrS-lkslZbeQ6oBK2GGgPiVtRK4S0HKd_NuehiCtg96dE5p8mQUL1YLcd4vpNFSxecWbOr_ixZwpmdqIIrU-41SNUuMxtWYvJi2k0A3v6gt1mM0qSs3B3iMB14jXqFZCnJQ?key=zareTrb70yP6YEeJ_Thf2ocC\" alt=\"\"\/><\/figure>\n\n\n\n<p>This process is repeated for a specified number of epochs or until the model achieves satisfactory performance.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"data-analysis-using-pytorch\"><\/span><strong>Data Analysis Using PyTorch<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Beyond deep learning, PyTorch is a powerful tool for data analysis tasks.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"loading-and-preprocessing-data\"><\/span><strong>Loading and Preprocessing Data<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>PyTorch provides utilities like Dataset and DataLoader to streamline data loading and preprocessing.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXdiN07fyHdGOqQlaOXs2Pekb6MfE2FbkGtKFIaqQp16kSOtLfiOPpQZvCiZmRMIl_5QcCvJPZojO6L_Q4oL9h-W-MjPEJJPbIBnh_LcNRv2B9ywjprcLrgshrrRtao0_veUSUx9?key=zareTrb70yP6YEeJ_Thf2ocC\" alt=\"\"\/><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"performing-data-analysis\"><\/span><strong>Performing Data Analysis<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>With data loaded, you can perform various analysis tasks, such as statistical computations, visualizations, or feeding the data into machine learning models.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXdwyBkwrMYnkb6KwbIz6fViUcjQp6p5_9cxfj-zS1R7KoiXQLVB9oGGsbpYL3Z8MKaNYpwriiWxTBhcY_OohzdOD8NuDr3PWp3N-xChfgGLMKBwJeAijJpwA60gDTZ2X4tJq84bRw?key=zareTrb70yP6YEeJ_Thf2ocC\" alt=\"\"\/><\/figure>\n\n\n\n<p>PyTorch&#8217;s tensor operations facilitate efficient and straightforward data analysis workflows.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"conclusion\"><\/span><strong>Conclusion<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>PyTorch: A Brief Overview PyTorch is an open-source deep learning framework that offers maximum flexibility and speed\u2002in building and training neural networks. The dynamic computation graphs, the powerful community support, and the native implementation of GPUs make it\u2002a convenient framework to pick for researchers and developers. With all the features, you can build, train, and analyze more complex models with\u2002ease. PyTorch Features Consistent and Efficient Numpy 1.1\u2002Part-1 If someone asks me to choose a single ML framework from many available for learning\/implementing AI &amp; ML, I will pick PyTorch without any thought.<\/p>\n\n\n\n<p><strong>Also Read: <a href=\"https:\/\/statanalytica.com\/blog\/python-vs-matlab-for-data-analysis\/\">Python vs MATLAB for Data Analysis: The Ultimate Comparison for 2025<\/a><\/strong><\/p>\n\n\n<div id=\"rank-math-faq\" class=\"rank-math-block\">\n<div class=\"rank-math-list \">\n<div id=\"faq-question-1738559947279\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><span class=\"ez-toc-section\" id=\"is-pytorch-better-than-tensorflow\"><\/span><strong>Is PyTorch better than TensorFlow?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Both frameworks have their advantages. PyTorch is known for its ease of use and flexibility, making it great for research, while TensorFlow is widely used in production environments due to its scalability and deployment capabilities.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1738559964460\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><span class=\"ez-toc-section\" id=\"can-pytorch-run-on-a-cpu\"><\/span><strong>Can PyTorch run on a CPU?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Yes, PyTorch can run on both CPUs and GPUs. While using a GPU accelerates computations, PyTorch still performs well on a CPU for smaller models and datasets.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1738559979053\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><span class=\"ez-toc-section\" id=\"what-are-pytorch-tensors\"><\/span><strong>What are PyTorch tensors?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Tensors are the fundamental data structure in PyTorch, similar to <a href=\"https:\/\/numpy.org\/doc\/2.1\/reference\/generated\/numpy.array.html\" target=\"_blank\" rel=\"noreferrer noopener\">NumPy arrays<\/a> but optimized for deep learning applications, with built-in GPU support.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Selecting a framework is very important for deep learning and data analysis \u2014 especially\u2002because many deep learning frameworks are still either new or changing rapidly. Recently, PyTorch has become one of the most\u2002usable frameworks among researchers and practitioners due to its flexibility, easy design, and functionality. This guide explains how you can use PyTorch for [&hellip;]<\/p>\n","protected":false},"author":16,"featured_media":37739,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[138],"tags":[5084,5083],"class_list":["post-37737","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-programming","tag-pytorch-for-data-analysis","tag-pytorch-for-deep-learning"],"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/posts\/37737","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/users\/16"}],"replies":[{"embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/comments?post=37737"}],"version-history":[{"count":1,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/posts\/37737\/revisions"}],"predecessor-version":[{"id":37740,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/posts\/37737\/revisions\/37740"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/media\/37739"}],"wp:attachment":[{"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/media?parent=37737"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/categories?post=37737"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/tags?post=37737"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}