{"id":2505,"date":"2024-09-18T15:17:00","date_gmt":"2024-09-18T06:17:00","guid":{"rendered":"https:\/\/secondlife.lol\/?p=2505"},"modified":"2024-09-18T15:19:35","modified_gmt":"2024-09-18T06:19:35","slug":"tensorflow-installation-guide-and-use-cases","status":"publish","type":"post","link":"https:\/\/secondlife.lol\/en\/tensorflow-installation-guide-and-use-cases\/","title":{"rendered":"Installing TensorFlow: The first step in AI development"},"content":{"rendered":"<p class=\"wp-block-paragraph\">With the rise of artificial intelligence and deep learning, many people are wondering how to install TensorFlow. TensorFlow is an open-source library for deep learning development that makes it easy to implement and train complex models. From installation to use cases, this article will teach you how to get started with TensorFlow. We'll walk you through it step-by-step so you don't miss any important information, so follow along!<\/p>\n\n\n<style>.kb-table-of-content-nav.kb-table-of-content-id83_5f28a6-34 .kb-table-of-content-wrap{padding-top:var(--global-kb-spacing-sm, 1.5rem);padding-right:var(--global-kb-spacing-sm, 1.5rem);padding-bottom:var(--global-kb-spacing-sm, 1.5rem);padding-left:var(--global-kb-spacing-sm, 1.5rem);border-top:3px solid var(--global-palette2, #2B6CB0);border-right:3px solid var(--global-palette2, #2B6CB0);border-bottom:3px solid var(--global-palette2, #2B6CB0);border-left:3px solid var(--global-palette2, #2B6CB0);border-top-left-radius:5px;border-top-right-radius:5px;border-bottom-right-radius:5px;border-bottom-left-radius:5px;box-shadow:0px 0px 14px 0px rgba(0, 0, 0, 0.2);}.kb-table-of-content-nav.kb-table-of-content-id83_5f28a6-34 .kb-table-of-contents-title-wrap{padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.kb-table-of-content-nav.kb-table-of-content-id83_5f28a6-34 .kb-table-of-contents-title-wrap{color:var(--global-palette2, #2B6CB0);}.kb-table-of-content-nav.kb-table-of-content-id83_5f28a6-34 .kb-table-of-contents-title{color:var(--global-palette2, #2B6CB0);font-size:28px;font-weight:regular;font-style:normal;}.kb-table-of-content-nav.kb-table-of-content-id83_5f28a6-34 .kb-table-of-content-wrap .kb-table-of-content-list{color:var(--global-palette1, #3182CE);line-height:2em;font-weight:regular;font-style:normal;margin-top:var(--global-kb-spacing-sm, 1.5rem);margin-right:0px;margin-bottom:0px;margin-left:0px;}.kb-table-of-content-nav.kb-table-of-content-id83_5f28a6-34 .kb-table-of-content-wrap .kb-table-of-content-list .kb-table-of-contents__entry:hover{color:var(--global-palette6, #718096);}@media all and (max-width: 1024px){.kb-table-of-content-nav.kb-table-of-content-id83_5f28a6-34 .kb-table-of-content-wrap{border-top:3px solid var(--global-palette2, #2B6CB0);border-right:3px solid var(--global-palette2, #2B6CB0);border-bottom:3px solid var(--global-palette2, #2B6CB0);border-left:3px solid var(--global-palette2, #2B6CB0);}}@media all and (max-width: 1024px){.kb-table-of-content-nav.kb-table-of-content-id83_5f28a6-34 .kb-table-of-contents-title{font-size:28px;}}@media all and (max-width: 767px){.kb-table-of-content-nav.kb-table-of-content-id83_5f28a6-34 .kb-table-of-content-wrap{border-top:3px solid var(--global-palette2, #2B6CB0);border-right:3px solid var(--global-palette2, #2B6CB0);border-bottom:3px solid var(--global-palette2, #2B6CB0);border-left:3px solid var(--global-palette2, #2B6CB0);}.kb-table-of-content-nav.kb-table-of-content-id83_5f28a6-34 .kb-table-of-contents-title{font-size:28px;}}<\/style>\n\n\n<h2 class=\"wp-block-heading\">How to install TensorFlow<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">There are several ways to install TensorFlow, but the simplest is to use the <strong>pip<\/strong> TensorFlow is available in CPU and GPU versions, so you'll need to install it for your system.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">1. basic installation (CPU version)<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The default version of TensorFlow is the one that runs on the CPU, which is sufficient for small-scale experiments with deep learning models or for environments with only a CPU. You can install it with the command below.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>pip install tensorflow\n\n# installation result (below is the result of reinstalling from the existing installation)\n(myenv) C:\\projects\\Project_Python&gt;pip install tensorflow\nRequirement already satisfied: tensorflow in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (2.16.1)\nRequirement already satisfied: tensorflow-intel==2.16.1 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorflow) (2.16.1)\nRequirement already satisfied: absl-py&gt;=1.0.0 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorflow-intel==2.16.1-&gt;tensorflow) (2.1.0)\nRequirement already satisfied: astunparse&gt;=1.6.0 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorflow-intel==2.16.1-&gt;tensorflow) (1.6.3)\nRequirement already satisfied: flatbuffers&gt;=23.5.26 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorflow-intel==2.16.1-&gt;tensorflow) (24.3.25)\nRequirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,&gt;=0.2.1 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorflow-intel==2.16.1-&gt;tensorflow) (0.5.4)\nRequirement already satisfied: google-pasta&gt;=0.1.1 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorflow-intel==2.16.1-&gt;tensorflow) (0.2.0)\nRequirement already satisfied: h5py&gt;=3.10.0 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorflow-intel==2.16.1-&gt;tensorflow) (3.11.0)\nRequirement already satisfied: libclang&gt;=13.0.0 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorflow-intel==2.16.1-&gt;tensorflow) (18.1.1)\nRequirement already satisfied: ml-dtypes~=0.3.1 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorflow-intel==2.16.1-&gt;tensorflow) (0.3.2)\nRequirement already satisfied: opt-einsum&gt;=2.3.2 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorflow-intel==2.16.1-&gt;tensorflow) (3.3.0)\nRequirement already satisfied: packaging in c:\\users\\user\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.16.1-&gt;tensorflow) (23.0)\nRequirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,=3.20.3 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorflow-intel==2.16.1-&gt;tensorflow) (4.25.3)\nRequirement already satisfied: requests=2.21.0 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorflow-intel==2.16.1-&gt;tensorflow) (2.31.0)\nRequirement already satisfied: setuptools in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorflow-intel==2.16.1-&gt;tensorflow) (70.1.1)\nRequirement already satisfied: six&gt;=1.12.0 in c:\\users\\user\\appdata\\roaming\\python\\python311\\site-packages (from tensorflow-intel==2.16.1-&gt;tensorflow) (1.16.0)\nRequirement already satisfied: termcolor&gt;=1.1.0 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorflow-intel==2.16.1-&gt;tensorflow) (2.4.0)\nRequirement already satisfied: typing-extensions&gt;=3.6.6 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorflow-intel==2.16.1-&gt;tensorflow) (4.10.0)\nRequirement already satisfied: wrapt&gt;=1.11.0 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorflow-intel==2.16.1-&gt;tensorflow) (1.16.0)\nRequirement already satisfied: grpcio=1.24.3 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorflow-intel==2.16.1-&gt;tensorflow) (1.64.1)\nRequirement already satisfied: tensorboard=2.16 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorflow-intel==2.16.1-&gt;tensorflow) (2.16.2)\nRequirement already satisfied: keras&gt;=3.0.0 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorflow-intel==2.16.1-&gt;tensorflow) (3.4.0)\nRequirement already satisfied: tensorflow-io-gcs-filesystem&gt;=0.23.1 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorflow-intel==2.16.1-&gt;tensorflow) (0.31.0)\nRequirement already satisfied: numpy=1.23.5 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorflow-intel==2.16.1-&gt;tensorflow) (1.26.4)\nRequirement already satisfied: wheel=0.23.0 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from astunparse&gt;=1.6.0-&gt;tensorflow-intel==2.16.1-&gt;tensorflow) (0.41.2)\nRequirement already satisfied: rich in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from keras&gt;=3.0.0-&gt;tensorflow-intel==2.16.1-&gt;tensorflow) (13.7.1)\nRequirement already satisfied: namex in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from keras&gt;=3.0.0-&gt;tensorflow-intel==2.16.1-&gt;tensorflow) (0.0.8)\nRequirement already satisfied: optree in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from keras&gt;=3.0.0-&gt;tensorflow-intel==2.16.1-&gt;tensorflow) (0.11.0)\nRequirement already satisfied: charset-normalizer=2 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from requests=2.21.0-&gt;tensorflow-intel==2.16.1-&gt;tensorflow) (3.3.2)\nRequirement already satisfied: idna=2.5 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from requests=2.21.0-&gt;tensorflow-intel==2.16.1-&gt;tensorflow) (3.6)\nRequirement already satisfied: urllib3=1.21.1 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from requests=2.21.0-&gt;tensorflow-intel==2.16.1-&gt;tensorflow) (2.1.0)\nRequirement already satisfied: certifi&gt;=2017.4.17 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from requests=2.21.0-&gt;tensorflow-intel==2.16.1-&gt;tensorflow) (2023.11.17)\nRequirement already satisfied: markdown&gt;=2.6.8 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorboard=2.16-&gt;tensorflow-intel==2.16.1-&gt;tensorflow) (3.6)\nRequirement already satisfied: tensorboard-data-server=0.7.0 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorboard=2.16-&gt;tensorflow-intel==2.16.1-&gt;tensorflow) (0.7.2)\nRequirement already satisfied: werkzeug&gt;=1.0.1 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from tensorboard=2.16-&gt;tensorflow-intel==2.16.1-&gt;tensorflow) (3.0.3)\nRequirement already satisfied: MarkupSafe&gt;=2.1.1 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from werkzeug&gt;=1.0.1-&gt;tensorboard=2.16-&gt;tensorflow-intel==2.16.1-&gt;tensorflow) (2.1.3)\nRequirement already satisfied: markdown-it-py&gt;=2.2.0 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from rich-&gt;keras&gt;=3.0.0-&gt;tensorflow-intel==2.16.1-&gt;tensorflow) (3.0.0)\nRequirement already satisfied: pygments=2.13.0 in c:\\users\\user\\appdata\\roaming\\python\\python311\\site-packages (from rich-&gt;keras&gt;=3.0.0-&gt;tensorflow-intel==2.16.1-&gt;tensorflow) (2.14.0)\nRequirement already satisfied: mdurl~=0.1 in c:\\users\\user\\.conda\\envs\\myenv\\lib\\site-packages (from markdown-it-py&gt;=2.2.0-&gt;rich-&gt;keras&gt;=3.0.0-&gt;tensorflow-intel==2.16.1-&gt;tensorflow) (0.1.2)<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\">2. Install the GPU version<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">If <strong>GPU<\/strong>then installing the GPU version of TensorFlow will significantly improve performance. However, installing the GPU version requires <strong>CUDA<\/strong>and <strong>cuDNN<\/strong> Make sure your GPU is supported before installing.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>pip install tensorflow-gpu<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\">Checking your TensorFlow version<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Once you're done installing, it's also a good idea to know how to check the version of TensorFlow you have installed, as you may have projects or libraries that require a specific version of TensorFlow.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Checking the version is easy with the command below.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import tensorflow as tf\nprint(tf.__version__)<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">Running this code will output the installed version of TensorFlow as shown below.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><kbd>Messages starting with oneDNN custom operations are on are logs generated by TensorFlow that inform you that TensorFlow is using custom operations from the Deep Neural Network Library (oneDNN). This is done to optimize CPU performance by using the <strong>oneDNN<\/strong>is used to help speed up the computation, but in doing so, the <strong>Order of floating-point operations<\/strong>Depending on the <strong>Subtle differences<\/strong>may occur. This is a warning that floating-point operations can have subtle differences depending on the order of computation, so you shouldn't expect exactly the same results. In most cases, you shouldn't care about this log because it's information for performance optimization, <strong>To turn off oneDNN optimization<\/strong> <code>tf_enable_onednn_options=0<\/code> You can disable oneDNN by setting an environment variable. This allows you to change the order of operations to avoid small floating point errors.<\/kbd><\/p>\n\n\n<style>.kb-image2505_ad1e86-22 .kb-image-has-overlay:after{opacity:0.3;border-top-left-radius:5px;border-top-right-radius:5px;border-bottom-right-radius:5px;border-bottom-left-radius:5px;}.kb-image2505_ad1e86-22 img.kb-img, .kb-image2505_ad1e86-22 .kb-img img{border-top-left-radius:5px;border-top-right-radius:5px;border-bottom-right-radius:5px;border-bottom-left-radius:5px;box-shadow:0px 0px 14px 0px rgba(0, 0, 0, 0.2);}<\/style>\n<div class=\"wp-block-kadence-image kb-image2505_ad1e86-22\"><figure class=\"aligncenter size-full\"><img decoding=\"async\" src=\"https:\/\/ailib.secondlife.lol\/wp-content\/uploads\/2024\/09\/image-5.jpg\" alt=\"\ud150\uc11c\ud50c\ub85c\uc6b0 \uc124\uce58 \ud6c4 \ubc84\uc804 \uc778\" class=\"kb-img wp-image-2801\"\/><\/figure><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Setting up TensorFlow<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">To check if TensorFlow works well on GPUs, you can run the code below to see if your GPU is being recognized correctly.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import tensorflow as tf\nprint(\"GPU is\", \"available\" if tf.config.list_physical_devices('GPU') else \"NOT available\")<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">If you see \"GPU is available\" when you run this code, it means that TensorFlow is using your GPU. If your GPU is not recognized, there may have been a problem with your CUDA and cuDNN installation, so double-check.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">TensorFlow Use Cases<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">TensorFlow is used in a variety of AI projects, including image classification, natural language processing, and autonomous driving. Here are a few examples.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\"><strong>1. categorize images<\/strong><\/h5>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Categorizing images<\/strong>is a classic use case for TensorFlow. <strong><a href=\"https:\/\/www.tensorflow.org\/datasets\/catalog\/mnist\" target=\"_blank\" rel=\"noopener\">MNIST<\/a><\/strong>, <strong>CIFAR-10<\/strong>, <strong>ImageNet<\/strong> The same image dataset can be used to build deep learning models, which are used to automatically classify images. This allows you to automatically process and classify image data for object recognition, face recognition, disease diagnosis, and more.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Example<\/strong>: <strong>Google Photos<\/strong>leverages deep learning models powered by TensorFlow to automatically categorize and tag photos. It recognizes people, places, objects, and more in your photos to make them easier to search and organize.<\/li>\n<\/ul>\n\n\n\n<h5 class=\"wp-block-heading\"><strong>2. natural language processing<\/strong><\/h5>\n\n\n\n<p class=\"wp-block-paragraph\">Natural language processing (NLP) is the art of analyzing and processing textual data, and TensorFlow plays an important role in this field. <strong>Categorizing text<\/strong>, <strong>Create text<\/strong>, <strong>Translation<\/strong>, <strong>Chatbots<\/strong> For many of the same tasks, TensorFlow is effective at processing and training on large amounts of textual data.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Google Translate<\/strong>The Google Translate system uses TensorFlow to automate translations between multiple languages, enabling natural translations in real-time. <strong>BERT<\/strong>, <strong>GPT<\/strong>have been implemented in TensorFlow to demonstrate better language understanding and generation capabilities.<\/li>\n<\/ul>\n\n\n\n<h5 class=\"wp-block-heading\"><strong>3. autonomous driving<\/strong><\/h5>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Autonomous vehicles<\/strong>must analyze and respond to various situations on the road in real time. Data collected by sensors such as cameras, LiDAR, and radar is processed through deep learning models based on tensorflow to recognize vehicles, pedestrians, traffic lights, and more in the environment.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Tesla Autopilot System<\/strong>: Tesla's self-driving cars use deep learning-based computer vision technology to recognize the road environment in real time. TensorFlow is used to analyze their surroundings and predict their path in real time while driving. Using TensorFlow's image recognition models, self-driving cars can recognize and avoid objects such as pedestrians and vehicles.<\/li>\n<\/ul>\n\n\n<style>.kb-image2505_c1acb8-cc .kb-image-has-overlay:after{opacity:0.3;border-top-left-radius:5px;border-top-right-radius:5px;border-bottom-right-radius:5px;border-bottom-left-radius:5px;}.kb-image2505_c1acb8-cc img.kb-img, .kb-image2505_c1acb8-cc .kb-img img{border-top-left-radius:5px;border-top-right-radius:5px;border-bottom-right-radius:5px;border-bottom-left-radius:5px;box-shadow:0px 0px 14px 0px rgba(0, 0, 0, 0.2);}<\/style>\n<div class=\"wp-block-kadence-image kb-image2505_c1acb8-cc\"><figure class=\"aligncenter size-medium\"><img decoding=\"async\" src=\"https:\/\/ailib.secondlife.lol\/wp-content\/uploads\/2024\/09\/image-6-300x300.jpg\" alt=\"\ud150\uc11c\ud50c\ub85c\uc6b0 \ud65c\uc6a9 \uc0ac\ub840 \uc774\ubbf8\uc9c0\" class=\"kb-img wp-image-2803\"\/><\/figure><\/div>\n\n\n\n<h5 class=\"wp-block-heading\"><strong>4. medical image analysis<\/strong><\/h5>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Medical image analytics<\/strong>TensorFlow is useful for analyzing medical images and diagnosing diseases. Medical images such as X-rays, MRIs, and CT scans can be analyzed to detect diseases such as cancer, tumors, and more at an early stage. Deep learning models can learn from tons of medical data to make diagnoses faster and more accurately than human experts.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Stanford University's AI for skin cancer diagnosis<\/strong>A Stanford University research team has developed a model for diagnosing skin cancer using TensorFlow, which uses deep learning to learn about various skin conditions and then performs expert-level diagnoses.<\/li>\n\n\n\n<li><strong>COVID-19 Diagnosis<\/strong>During the COVID-19 pandemic, TensorFlow was used to analyze X-ray images to detect signs of lung disease. This allowed healthcare providers to quickly assess a patient's condition and determine the appropriate treatment.<\/li>\n<\/ul>\n\n\n\n<h5 class=\"wp-block-heading\"><strong>5. Recommendation system<\/strong><\/h5>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Recommendation system<\/strong>is a technology that delivers personalized content based on user behavioral data. TensorFlow is used to build systems that learn from large amounts of data to recommend the right products, videos, music, and more for each user.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Netflix Recommendation System<\/strong>: Netflix uses TensorFlow to recommend new content based on the movies and shows you've watched. TensorFlow's deep learning models analyze viewing patterns and preferences to deliver personalized recommendations to each user in real time.<\/li>\n\n\n\n<li><strong>Amazon's personalized recommendations<\/strong>Amazon uses a recommendation system powered by TensorFlow to recommend products that users are more likely to buy. This improves the shopping experience and helps keep customers coming back for more.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Conclusion: From installing to utilizing TensorFlow<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In this article, we've covered everything from how to install TensorFlow to GPU settings, version checking, and how to use TensorFlow in practice. TensorFlow is an invaluable tool for those new to deep learning and for advanced researchers alike. Now that you've installed TensorFlow, you're ready to get started. Once you've installed TensorFlow, you're ready to get started with your AI project!<\/p>","protected":false},"excerpt":{"rendered":"<p>\uc778\uacf5\uc9c0\ub2a5\uacfc \ub525\ub7ec\ub2dd\uc774 \uc8fc\ubaa9\ubc1b\uc73c\uba74\uc11c \ud150\uc11c\ud50c\ub85c\uc6b0 \uc124\uce58\uc5d0 \ub300\ud574 \uad81\uae08\ud574\ud558\ub294 \ubd84\ub4e4\uc774 \ub9ce\uc544\uc9c0\uace0 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