{"id":215,"date":"2023-05-17T05:59:17","date_gmt":"2023-05-17T05:59:17","guid":{"rendered":"https:\/\/www.intellisofttechnologies.com\/blog\/?p=215"},"modified":"2023-05-17T09:11:40","modified_gmt":"2023-05-17T09:11:40","slug":"what-are-neural-networks-all-you-need-to-know-to-get-started","status":"publish","type":"post","link":"https:\/\/www.intellisofttechnologies.com\/blog\/2023\/05\/17\/what-are-neural-networks-all-you-need-to-know-to-get-started\/","title":{"rendered":"What Are Neural Networks? All You Need to Know to Get Started!"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>If you are a beginner trying to understand what Neural Networks are, how they work, and why they are revolutionizing the field of artificial intelligence, then you&#8217;ve come to the right place. We will be decoding the basics of neural networks in layman\u2019s terms so that even non-technical people can get the hype.<\/em><\/p>\n\n\n\n<div class=\"wp-block-aioseo-table-of-contents\"><ul><li><a href=\"#aioseo-what-are-neural-networks\">What are Neural Networks?<\/a><ul><li><a href=\"#aioseo-2-how-do-neural-networks-work\">How do Neural Networks work?<\/a><ul><li><a href=\"#aioseo-types-of-neural-networks\">Types of Neural Networks<\/a><\/li><\/ul><\/li><\/ul><\/li><\/ul><\/div>\n\n\n\n<blockquote class=\"wp-block-quote has-text-align-center is-style-default has-white-color has-vivid-red-background-color has-text-color has-background has-medium-font-size is-layout-flow wp-block-quote-is-layout-flow\" style=\"font-style:normal;font-weight:500\">\n<p class=\"has-white-color has-text-color has-medium-font-size wp-block-paragraph\" style=\"text-transform:capitalize\"><strong>   &#8220;Machine learning is the science of getting computers to act without being explicitly programmed.&#8221; <\/strong><\/p>\n<cite><strong>Arthur Samuel<\/strong><\/cite><\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">This is precisely what neural networks do: they learn from experience and data and make decisions based on that learning. <\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignleft size-medium is-resized\" id=\"NeuralNetworks_Intellisofttechnologies\"><img data-dominant-color=\"0e0a0d\" data-has-transparency=\"true\" style=\"--dominant-color: #0e0a0d;\" decoding=\"async\" src=\"https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/brain-g21e88a7a8_1280-272x300.webp\" alt=\"Neural networks\" class=\"has-transparency wp-image-268\" width=\"180\" height=\"198\" title=\"Neural Networks are modelled after the human brain\" srcset=\"https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/brain-g21e88a7a8_1280-272x300.webp 272w, https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/brain-g21e88a7a8_1280-928x1024.webp 928w, https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/brain-g21e88a7a8_1280-768x847.webp 768w, https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/brain-g21e88a7a8_1280-1024x1130.webp 1024w, https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/brain-g21e88a7a8_1280-600x662.webp 600w, https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/brain-g21e88a7a8_1280.webp 1160w\" sizes=\"(max-width: 180px) 100vw, 180px\" \/><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"has-black-color has-text-color wp-block-paragraph\">They are trained using large datasets that provide examples of the tasks they are supposed to perform. Further, the network adjusts its internal parameters, called weights, to enhance the accuracy and quality of its predictions.<\/p>\n\n\n\n<p class=\"has-black-color has-text-color wp-block-paragraph\">Neural Networks are modelled after the human brain and consist of layers of interconnected nodes, each performing a specific task. Initially developed to resolve intricate issues, recognize patterns, and identify concealed patterns in unprocessed data, neural networks have evolved to serve a range of diverse applications, from computer vision and <a href=\"https:\/\/www.intellisofttechnologies.com\/blog\/post\/2023\/02\/21\/speech-recognition-and-its-dilemmas\" target=\"_blank\" rel=\"noopener\" title=\"\">speech recognition<\/a> to self-driving cars and fraud detection.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"has-black-color has-text-color wp-block-paragraph\">Now, let\u2019s dive in and understand what neural networks are.<\/p>\n\n\n\n<h1 class=\"wp-block-heading\" id=\"aioseo-what-are-neural-networks\"><strong>What are Neural Networks?<\/strong><\/h1>\n\n\n\n<p class=\"wp-block-paragraph\">                                               <\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img data-dominant-color=\"e5d8d8\" data-has-transparency=\"false\" style=\"--dominant-color: #e5d8d8;\" decoding=\"async\" src=\"https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/Screenshot-2023-03-27-122943.webp\" alt=\"What are neural networks?\" class=\"not-transparent wp-image-272\" width=\"377\" height=\"324\"\/><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"has-black-color has-text-color wp-block-paragraph\">Artificial Neural Networks (ANNs) also known as Simulated Neural Networks (SNNs) are a subset of machine learning (ML). Their core functionality mimics the signal-based communication style of biological neurons. Some of the characteristics of Neural Networks include:<\/p>\n\n\n\n<ul class=\"has-black-color has-text-color wp-block-list\">\n<li style=\"text-transform:none\">They consist of layers of interconnected nodes, each performing a specific task. <\/li>\n\n\n\n<li style=\"text-transform:none\">They can learn from large datasets and recognize patterns that are too complicated for humans to detect.<\/li>\n\n\n\n<li style=\"text-transform:none\">Neural networks can cluster and make sense of big data at high velocity.<\/li>\n\n\n\n<li style=\"text-transform:none\">They can be trained to perform various tasks, such as natural language processing (NLP), signal separation, image recognition, speech recognition, and predictive modelling.<\/li>\n\n\n\n<li style=\"text-transform:none\">Almost all industries are using neural network technology to enhance their decision-making processes, from marketing, defence, manufacturing, banking, retail and consumer goods, autonomous vehicles, and healthcare to finance.<\/li>\n<\/ul>\n\n\n\n<p class=\"has-vivid-red-color has-text-color wp-block-paragraph\"><em>Some examples of popular neural networks are AlexNet, ResNet, VGGNet, and the trending GPT (Generative Pre-trained Transformer) by OpenAI.&nbsp;<\/em><\/p>\n\n\n\n<p class=\"has-black-color has-text-color wp-block-paragraph\">Now that you have a fair understanding of ANNs, let\u2019s closely examine how they work.<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-large-font-size\" id=\"aioseo-2-how-do-neural-networks-work\"><strong>How do Neural Networks work?<\/strong><\/h2>\n\n\n\n<p class=\"has-black-color has-text-color wp-block-paragraph\">Neural networks are powered by artificial neurons that are tiny, interconnected nodes or units arranged in a series of connected layers: input, target or hidden layer(s), and output layer. Here&#8217;s a quick breakdown of how they work:<\/p>\n\n\n\n<ul class=\"has-black-color has-text-color wp-block-list\">\n<li><strong>Input layer<\/strong>: the first layer of the network, which receives inputs or information from the outside world and then further classifies or interprets the received data.<\/li>\n\n\n\n<li><strong>Hidden layers<\/strong>: one or more layers are present between the input and output layers that process the inputs and make predictions accordingly.<\/li>\n\n\n\n<li><strong>Output layer<\/strong>: the final layer of the network, which produces the output or response based on the inputs and the weights assigned to each node.<\/li>\n<\/ul>\n\n\n\n<p class=\"has-vivid-red-color has-text-color wp-block-paragraph\">Like a biological neuron, nodes get activated only upon receiving external stimuli or input (usually from the CPU) and respond by generating an output.&nbsp;<\/p>\n\n\n\n<p class=\"has-black-color has-text-color wp-block-paragraph\">The connection between individual nodes is known as \u2018weights\u2019 and can be positive or negative. These weights act as the connecting passages for the signals to flow from one node to another. If the output of a particular node surpasses the predetermined threshold (or bias) value, it \u2018fires\u2019 or becomes active and transmits data to the subsequent layer of the network. This way, the output of one node becomes the input of another. However, if the output falls short of the threshold, the node remains inactive and fails to transmit any data to the next layer.&nbsp;<\/p>\n\n\n\n<p class=\"has-black-color has-text-color wp-block-paragraph\">To sum up, the weight of the input is directly proportional to the impact on the network. And the bias in neural networks is primarily used to offset or fine-tune the result.<\/p>\n\n\n\n<p class=\"has-text-align-center has-white-color has-vivid-red-background-color has-text-color has-background has-medium-font-size wp-block-paragraph\" style=\"font-style:normal;font-weight:600\"><strong>Output = Input*Weight + Bias<\/strong><\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img data-dominant-color=\"f1dfdf\" data-has-transparency=\"false\" style=\"--dominant-color: #f1dfdf;\" decoding=\"async\" src=\"https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/Screenshot-2023-03-27-122801.webp\" alt=\"How do neural networks work?\" class=\"not-transparent wp-image-273\" width=\"549\" height=\"314\" srcset=\"https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/Screenshot-2023-03-27-122801.webp 732w, https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/Screenshot-2023-03-27-122801-300x172.webp 300w, https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/Screenshot-2023-03-27-122801-600x343.webp 600w\" sizes=\"(max-width: 549px) 100vw, 549px\" \/><figcaption class=\"wp-element-caption\"><a href=\"http:\/\/jalammar.github.io\/visual-interactive-guide-basics-neural-networks\/\"><strong><sub>Source<\/sub><\/strong><\/a><\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"has-black-color has-text-color wp-block-paragraph\">Furthermore, when the number of hidden layers in a neural network increases, <strong><em>Deep Learning Neural Networks<\/em><\/strong> are formed. These networks process information through multiple layers of interconnected nodes, allowing for the extraction of increasingly complex features and patterns.<\/p>\n\n\n\n<p class=\"has-black-color has-text-color wp-block-paragraph\">One of the main advantages of deep learning neural networks is their ability to learn hierarchical representations of data. By breaking down the data into multiple layers, with each layer learning more abstract features than the previous layer, the network can learn to identify increasingly complex patterns. This hierarchical approach to learning allows deep learning neural networks to deliver phenomenal results on many tasks.<\/p>\n\n\n<div class=\"wp-block-image is-style-default\">\n<figure class=\"aligncenter size-full is-resized\"><img data-dominant-color=\"e8d2d2\" data-has-transparency=\"true\" style=\"--dominant-color: #e8d2d2;\" decoding=\"async\" src=\"https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/my-image.webp\" alt=\"Deep neural network diagram by Intellisoft Technologies\" class=\"has-transparency wp-image-284\" width=\"421\" height=\"395\" srcset=\"https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/my-image.webp 561w, https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/my-image-300x282.webp 300w\" sizes=\"(max-width: 421px) 100vw, 421px\" \/><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"has-black-color has-text-color wp-block-paragraph\">Just like any good product or service, feedback plays a huge role in training or teaching neural networks! By adjusting the weights in response to feedback, neural networks can identify patterns, make accurate predictions, adapt, and evolve over time. Feedback is also crucial during inference or testing to refine the network&#8217;s performance even further.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-black-color has-text-color has-large-font-size\" id=\"aioseo-types-of-neural-networks\"><strong>Types of Neural Networks<\/strong><\/h3>\n\n\n\n<p class=\"has-black-color has-text-color has-medium-font-size wp-block-paragraph\">Top 4 most commonly used neural networks:<\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:100%\">\n<div style=\"overflow: auto;\">\n\n<table class=\"has-white-color has-vivid-red-background-color has-text-color has-background\" style=\"width: 1000px; margin: 0 auto\">\n<thead>\n<tr>\n<th><strong>Name<\/strong><\/th>\n<th><strong>Description<\/strong><\/th>\n<th><strong>Application Examples<\/strong><\/th>\n<th><strong>What do they look like?<\/strong><\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Feedforward Neural Networks<\/strong><\/td>\n<td>Also known as multi-layer perceptrons (MLPs), are the easiest to understand. Data flows in a forward linear motion from input-&gt;output.&nbsp;<\/td>\n<td>Natural Language Processing (NLP), fraud detection, image recognition, etc.<\/td>\n<td><img decoding=\"async\" src=\"data:image\/png;base64,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\" width=\"210\" height=\"96\"><\/td>\n<\/tr>\n<tr>\n<td><strong>Recurrent Neural Networks (RNNs)<\/strong><\/td>\n<td>RNNs maintain or retain a \u2018memory\u2019 of past inputs and use it to inform future predictions. Their major strength is their unique feedback loop architecture.<\/td>\n<td>Speech-to-text conversion, stock market predictions, forecasting, etc.<\/td>\n<td><img decoding=\"async\" src=\"data:image\/png;base64,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\" width=\"210\" height=\"167\"><\/td>\n<\/tr>\n<tr>\n<td>\n<p><strong>Convolutional Neural Networks (CNNs)<\/strong><\/p>\n<p>&nbsp;<\/p>\n<\/td>\n<td>CNNs recognize or identify patterns in images using linear algebra principles.&nbsp;\n<p>&nbsp;<\/p>\n<p>Think of CNNs as a group of filters that are applied to an image, where each filter detects specific features such as shapes. These filters are then combined to recognize more complex features like objects and faces.<\/p>\n<\/td>\n<td>Computer vision, facial recognition, robotics, etc.<\/td>\n<td><img decoding=\"async\" src=\"data:image\/png;base64,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\" width=\"208\" height=\"199\"><br><a title=\"\" href=\"https:\/\/www.researchgate.net\/figure\/Schematic-diagram-of-a-basic-convolutional-neural-network-CNN-architecture-26_fig1_336805909\" target=\"_blank\" rel=\"noopener\"><em><sub><span style=\"text-decoration: underline;\">Source<\/span><\/sub><\/em><\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<h4 class=\"wp-block-heading has-large-font-size\" id=\"aioseo-bottom-line\"><strong>Bottom Line<\/strong><\/h4>\n\n\n\n<p class=\"has-black-color has-text-color wp-block-paragraph\">Neural networks are powerful tools that can transform industries and solve complex problems. But they also have their fair share of drawbacks and limitations, including the need for large amounts of data and computing power (let\u2019s not get into that today). <\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignleft size-full is-resized\"><img data-dominant-color=\"d8dce1\" data-has-transparency=\"false\" style=\"--dominant-color: #d8dce1;\" decoding=\"async\" src=\"https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/conclusion.webp\" alt=\"\" class=\"not-transparent wp-image-279\" width=\"732\" height=\"354\" srcset=\"https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/conclusion.webp 817w, https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/conclusion-300x145.webp 300w, https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/conclusion-768x371.webp 768w, https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/conclusion-600x290.webp 600w\" sizes=\"(max-width: 732px) 100vw, 732px\" \/><\/figure>\n<\/div>\n\n\n<p class=\"has-black-color has-text-color wp-block-paragraph\">Businesses can leverage neural networks to gain a competitive advantage by using them for analyzing large amounts of data, optimizing their operations, and improving customer experience via <a href=\"https:\/\/www.intellisofttechnologies.com\/blog\" target=\"_blank\" rel=\"noopener\" title=\"\">Sentiment Analysis<\/a>. It is estimated that by 2030, the adoption of AI and machine learning technologies by businesses can result in a whopping $15.7 trillion in business value (<a href=\"https:\/\/www.pwc.com\/gx\/en\/issues\/data-and-analytics\/publications\/artificial-intelligence-study.html\" target=\"_blank\" rel=\"noopener\" title=\"\">PwC<\/a>).<\/p>\n\n\n\n<p class=\"has-black-color has-text-color wp-block-paragraph\">In conclusion, businesses that invest in deep learning services and other machine learning technologies now will be better positioned to succeed in the future!&nbsp;<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div class=\"wp-block-query is-layout-flow wp-block-query-is-layout-flow\"><\/div>\n<\/div>\n<\/div>\n\n\n\n<p class=\"has-text-align-center has-black-color has-text-color has-medium-font-size wp-block-paragraph\" style=\"font-style:normal;font-weight:500\"><em>For more information on how you can leverage deep learning services to stay ahead of the curve, <a href=\"https:\/\/www.intellisofttechnologies.com\/Contact.shtml\" target=\"_blank\" rel=\"noreferrer noopener\">get in touch<\/a> with our experts today!<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Artificial Neural Networks (ANNs) also known as Simulated Neural Networks are a subset of machine learning. They are modelled after the human brain and consist of layers of interconnected nodes, each performing a specific task. <\/p>\n","protected":false},"author":1,"featured_media":263,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14,5,13],"tags":[6,15,7,16],"class_list":["post-215","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-machine-learning","category-neural-network","tag-ai","tag-learning-ai","tag-machine-learning","tag-neural-networks"],"aioseo_notices":[],"featured_media_urls":{"thumbnail":["https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/8b8d52de-5ef3-41c1-a9a3-ff57671767c6-e1684231571277-150x150.webp",150,150,true],"medium":["https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/8b8d52de-5ef3-41c1-a9a3-ff57671767c6-e1684231571277-300x300.webp",300,300,true],"medium_large":["https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/8b8d52de-5ef3-41c1-a9a3-ff57671767c6-e1684231494513-768x768.webp",640,640,true],"large":["https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/8b8d52de-5ef3-41c1-a9a3-ff57671767c6-e1684231571277.webp",600,600,false],"1536x1536":["https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/8b8d52de-5ef3-41c1-a9a3-ff57671767c6-e1684231571277.webp",600,600,false],"2048x2048":["https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/8b8d52de-5ef3-41c1-a9a3-ff57671767c6-e1684231571277.webp",600,600,false],"blogus-slider-full":["https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/8b8d52de-5ef3-41c1-a9a3-ff57671767c6-e1684231494513-880x720.webp",880,720,true],"blogus-featured":["https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/8b8d52de-5ef3-41c1-a9a3-ff57671767c6-e1684231571277.webp",600,600,false],"blogus-medium":["https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/8b8d52de-5ef3-41c1-a9a3-ff57671767c6-e1684231571277-600x380.webp",600,380,true],"portfolio_item-thumbnail":["https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/8b8d52de-5ef3-41c1-a9a3-ff57671767c6-e1684231571277-600x400.webp",600,400,true],"portfolio_item-masonry":["https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/8b8d52de-5ef3-41c1-a9a3-ff57671767c6-e1684231494513-600x600.webp",600,600,true],"portfolio_item-thumbnail_cinema":["https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/8b8d52de-5ef3-41c1-a9a3-ff57671767c6-e1684231571277-600x335.webp",600,335,true],"portfolio_item-thumbnail_portrait":["https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/8b8d52de-5ef3-41c1-a9a3-ff57671767c6-e1684231494513-600x880.webp",600,880,true],"portfolio_item-thumbnail_square":["https:\/\/www.intellisofttechnologies.com\/blog\/wp-content\/uploads\/2023\/05\/8b8d52de-5ef3-41c1-a9a3-ff57671767c6-e1684231494513-800x800.webp",800,800,true]},"_links":{"self":[{"href":"https:\/\/www.intellisofttechnologies.com\/blog\/wp-json\/wp\/v2\/posts\/215","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.intellisofttechnologies.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.intellisofttechnologies.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.intellisofttechnologies.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.intellisofttechnologies.com\/blog\/wp-json\/wp\/v2\/comments?post=215"}],"version-history":[{"count":87,"href":"https:\/\/www.intellisofttechnologies.com\/blog\/wp-json\/wp\/v2\/posts\/215\/revisions"}],"predecessor-version":[{"id":316,"href":"https:\/\/www.intellisofttechnologies.com\/blog\/wp-json\/wp\/v2\/posts\/215\/revisions\/316"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.intellisofttechnologies.com\/blog\/wp-json\/wp\/v2\/media\/263"}],"wp:attachment":[{"href":"https:\/\/www.intellisofttechnologies.com\/blog\/wp-json\/wp\/v2\/media?parent=215"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.intellisofttechnologies.com\/blog\/wp-json\/wp\/v2\/categories?post=215"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.intellisofttechnologies.com\/blog\/wp-json\/wp\/v2\/tags?post=215"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}