{"id":8893,"date":"2026-08-10T10:01:10","date_gmt":"2026-08-10T10:01:10","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/oreilly-tensorflow-in-action-video-edition-2022-10\/"},"modified":"2026-08-10T10:01:10","modified_gmt":"2026-08-10T10:01:10","slug":"oreilly-tensorflow-in-action-video-edition-2022-10","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/oreilly-tensorflow-in-action-video-edition-2022-10\/","title":{"rendered":"Oreilly \u2013 TensorFlow in Action, Video Edition 2022-10"},"content":{"rendered":"<div class=\"w-post-elm post_content\">\n<h2 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Description<\/span><\/h2>\n<p dir=\"ltr\" style=\"text-align: left\" data-sourcepos=\"7:1-7:687\"><span style=\"vertical-align: inherit\">TensorFlow in Action, Video Edition. This course teaches you the secrets of designing with TensorFlow to build successful deep learning applications. In this hands-on guide, Thushan Ganegedara, an active StackOverflow contributor on deep learning, walks you through the new features of TensorFlow 2. In this course, you\u2019ll not only master the fundamentals of TensorFlow, but also learn how to implement deep learning networks, choose a high-level Keras API for model building, write end-to-end data pipelines, build models for computer vision and natural language processing, use pre-trained NLP models, and get acquainted with new algorithms like transformers, attention models, and ElMo.<\/span><\/p>\n<h3 dir=\"ltr\" style=\"text-align: left\" data-sourcepos=\"9:1-9:25\"><span style=\"vertical-align: inherit\">What you will learn:<\/span><\/h3>\n<ul dir=\"ltr\" style=\"text-align: left\" data-sourcepos=\"11:1-18:0\">\n<li data-sourcepos=\"11:1-11:18\"><span style=\"vertical-align: inherit\">TensorFlow Basics<\/span><\/li>\n<li data-sourcepos=\"12:1-12:35\"><span style=\"vertical-align: inherit\">Implementing deep learning networks<\/span><\/li>\n<li data-sourcepos=\"13:1-13:42\"><span style=\"vertical-align: inherit\">Choosing the high-level Keras API for model building<\/span><\/li>\n<li data-sourcepos=\"14:1-14:37\"><span style=\"vertical-align: inherit\">Writing end-to-end data pipelines<\/span><\/li>\n<li data-sourcepos=\"15:1-15:61\"><span style=\"vertical-align: inherit\">Building models for computer vision and natural language processing<\/span><\/li>\n<li data-sourcepos=\"16:1-16:46\"><span style=\"vertical-align: inherit\">Using pre-trained NLP models<\/span><\/li>\n<li data-sourcepos=\"17:1-18:0\"><span style=\"vertical-align: inherit\">New algorithms such as transformers, attention models, and ElMo<\/span><\/li>\n<\/ul>\n<h3 dir=\"ltr\" style=\"text-align: left\" data-sourcepos=\"19:1-19:32\"><span style=\"vertical-align: inherit\">Who is this course suitable for?<\/span><\/h3>\n<ul dir=\"ltr\" style=\"text-align: left\">\n<li data-sourcepos=\"21:1-21:77\"><span style=\"vertical-align: inherit\">This course is suitable for Python programmers with basic deep learning skills.<\/span><\/li>\n<\/ul>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">TensorFlow in Action, Video Edition Course Specifications<\/span><\/h3>\n<ul dir=\"ltr\" style=\"text-align: left\">\n<li><span style=\"vertical-align: inherit\">Publisher: <\/span><a href=\"https:\/\/href.li\/?https:\/\/www.oreilly.com\/library\/view\/tensorflow-in-action\/9781617298349VE\/\" target=\"_blank\" rel=\"noopener\"><span style=\"vertical-align: inherit\">Oreilly<\/span><\/a><\/li>\n<li><span style=\"vertical-align: inherit\">Instructor: <\/span><a class=\"author-name\" href=\"https:\/\/downloadlynet.ir\/tag\/thushan-ganegedara\/\"><span style=\"vertical-align: inherit\">Thushan Ganegedara<\/span><\/a><\/li>\n<li><span style=\"vertical-align: inherit\">Training level: Beginner to advanced<\/span><\/li>\n<li><span style=\"vertical-align: inherit\">Training duration: 18 hours and 8 minutes<\/span><\/li>\n<\/ul>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Course headings<\/span><\/h3>\n<div class=\"ud-block-list-item ud-block-list-item-small ud-block-list-item-tight ud-block-list-item-neutral ud-text-sm\" dir=\"ltr\" style=\"text-align: left\">\n<ul>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 1. Foundations of TensorFlow 2 and deep learning<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. The amazing world of TensorFlow <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 1. GPU vs. CPU <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 1. When and when not to use TensorFlow <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 1. What will this book teach you? <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 1. Who is this book for? <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 1. Should we really care about Python and TensorFlow 2? <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 1. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. TensorFlow 2 <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 2. TensorFlow building blocks <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 2. Neural network-related computations in TensorFlow <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 2. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Keras and data retrieval in TensorFlow 2 <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 3. Retrieving data for TensorFlow\/Keras models <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 3. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Dipping toes in deep learning <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 4. Convolutional neural networks <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 4. One step at a time: Recurrent neural networks (RNNs) <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 4. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. State-of-the-art in deep learning: Transformers <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 5. Understanding the Transformer model <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 5. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 2. Look ma, no hands! Deep networks in the real world<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Teaching machines to see: Image classification with CNNs <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. Creating data pipelines using the Keras ImageDataGenerator <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. Inception net: Implementing a state-of-the-art image classifier <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. Training the model and evaluating performance <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 6. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Teaching machines to see better: Improving CNNs and making them confess <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. Toward minimalism: Minception instead of Inception <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. If you can\u2019t beat them, join \u2019em: Using pretrained networks for enhancing performance <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. Grad-CAM: Making CNNs confess <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 7. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Telling things apart: Image segmentation <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 8. Getting serious: Defining a TensorFlow data pipeline <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 8. DeepLabv3: Using pretrained networks to segment images <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 8. Compiling the model: Loss functions and evaluation metrics in image segmentation <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 8. Training the model <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 8. Evaluating the model <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 8. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Natural language processing with TensorFlow: Sentiment analysis <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 9. Getting text ready for the model <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 9. Defining an end-to-end NLP pipeline with TensorFlow <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 9. Happy reviews mean happy customers: Sentiment analysis <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 9. Training and evaluating the model <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 9. Injecting semantics with word vectors <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 9. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Natural language processing with TensorFlow: Language modeling <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 10. GRUs in Wonderland: Generating text with deep learning <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 10. Measuring the quality of the generated text <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 10. Training and evaluating the language model <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 10. Generating new text from the language model: Greedy decoding <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 10. Beam search: Enhancing the predictive power of sequential models <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 10. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 3. Advanced deep networks for complex problems<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Sequence-to-sequence learning: Part 1 <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 11. Writing an English-German seq2seq machine translator <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 11. Training and evaluating the model <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 11. From training to inference: Defining the inference model <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 11. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 12. Sequence-to-sequence learning: Part 2 <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 12. Visualizing the attention <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 12. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 13. Transformers <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 13. Using pretrained BERT for spam classification <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 13. Question answering with Hugging Face\u2019s Transformers <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 13. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 14. TensorBoard: Big brother of TensorFlow <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 14. Tracking and monitoring models with TensorBoard <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 14. Using tf.summary to write custom metrics during model training <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 14. Profiling models to detect performance bottlenecks <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 14. Visualizing word vectors with the TensorBoard <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 14. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 15. TFX: MLOps and deploying models with TensorFlow <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 15. Training a simple regression neural network: TFX Trainer API <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 15. Setting up Docker to serve a trained model <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 15. Deploying the model and serving it through an API <\/span><br \/><span style=\"vertical-align: inherit\">Chapter 15. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix A. Setting up the environment <\/span><br \/><span style=\"vertical-align: inherit\">Appendix A. In Windows Environments <\/span><br \/><span style=\"vertical-align: inherit\">Appendix A. Activating and deactivating the conda environment <\/span><br \/><span style=\"vertical-align: inherit\">Appendix A. Running the Jupyter Notebook server and creating notebooks <\/span><br \/><span style=\"vertical-align: inherit\">Appendix A. Miscellaneous notes<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix B. Computer vision <\/span><br \/><span style=\"vertical-align: inherit\">Appendix B. Image segmentation: U-Net model<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix C. Natural language processing<\/span><\/li>\n<\/ul>\n<\/div>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Course images<\/span><\/h3>\n<p dir=\"ltr\" style=\"text-align: left\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-967364 size-full\" src=\"https:\/\/downloadly.ir\/wp-content\/uploads\/2025\/03\/TensorFlow-in-Action-Video-Edition.png\" alt=\"TensorFlow in Action, Video Edition\" width=\"1252\" height=\"441\"><\/p>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Sample course video<\/span><\/h3>\n<div style=\"width: 640px;\" class=\"wp-video\"><span class=\"mejs-offscreen\">Video Player<\/span><\/p>\n<div id=\"mep_0\" class=\"mejs-container mejs-container-keyboard-inactive wp-video-shortcode mejs-video\" tabindex=\"0\" role=\"application\" aria-label=\"Video Player\" style=\"width: 640px; height: 360px; min-width: 217px;\">\n<div class=\"mejs-inner\">\n<div class=\"mejs-mediaelement\"><mediaelementwrapper id=\"video-157060-1\"><video class=\"wp-video-shortcode\" id=\"video-157060-1_html5\" width=\"640\" height=\"360\" preload=\"metadata\" src=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/TensorFlow_in_Action_Video_Edition_Downloadly.ir.mp4?_=1\" style=\"width: 640px; height: 360px;\"><source type=\"video\/mp4\" src=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/TensorFlow_in_Action_Video_Edition_Downloadly.ir.mp4?_=1\"><a href=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/TensorFlow_in_Action_Video_Edition_Downloadly.ir.mp4?nocache=1786113606031\">https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/TensorFlow_in_Action_Video_Edition_Downloadly.ir.mp4<\/a><\/video><\/mediaelementwrapper><\/div>\n<div class=\"mejs-layers\">\n<div class=\"mejs-poster mejs-layer\" style=\"display: none; width: 100%; height: 100%;\"><\/div>\n<div class=\"mejs-overlay mejs-layer\" style=\"display: none; width: 100%; height: 100%;\">\n<div class=\"mejs-overlay-loading\"><span class=\"mejs-overlay-loading-bg-img\"><\/span><\/div>\n<\/div>\n<div class=\"mejs-overlay mejs-layer\" style=\"display: none; width: 100%; height: 100%;\">\n<div class=\"mejs-overlay-error\"><\/div>\n<\/div>\n<div class=\"mejs-overlay mejs-layer mejs-overlay-play\" style=\"width: 100%; height: 100%;\">\n<div class=\"mejs-overlay-button\" role=\"button\" tabindex=\"0\" aria-label=\"Play\" aria-pressed=\"false\"><\/div>\n<\/div>\n<\/div>\n<div class=\"mejs-controls\">\n<div class=\"mejs-button mejs-playpause-button mejs-play\"><button type=\"button\" aria-controls=\"mep_0\" title=\"Play\" aria-label=\"Play\" tabindex=\"0\"><\/button><\/div>\n<div class=\"mejs-time mejs-currenttime-container\" role=\"timer\" aria-live=\"off\"><span class=\"mejs-currenttime\">00:00<\/span><\/div>\n<div class=\"mejs-time-rail\"><span class=\"mejs-time-total mejs-time-slider\" role=\"slider\" tabindex=\"0\" aria-label=\"Time Slider\" aria-valuemin=\"0\" aria-valuemax=\"0\" aria-valuenow=\"0\" aria-valuetext=\"00:00\"><span class=\"mejs-time-buffering\" style=\"display: none;\"><\/span><span class=\"mejs-time-loaded\"><\/span><span class=\"mejs-time-current\"><\/span><span class=\"mejs-time-hovered no-hover\"><\/span><span class=\"mejs-time-handle\"><span class=\"mejs-time-handle-content\"><\/span><\/span><span class=\"mejs-time-float\"><span class=\"mejs-time-float-current\">00:00<\/span><span class=\"mejs-time-float-corner\"><\/span><\/span><\/span><\/div>\n<div class=\"mejs-time mejs-duration-container\"><span class=\"mejs-duration\">00:00<\/span><\/div>\n<div class=\"mejs-button mejs-volume-button mejs-mute\"><button type=\"button\" aria-controls=\"mep_0\" title=\"Mute\" aria-label=\"Mute\" tabindex=\"0\"><\/button><a href=\"javascript:void(0);\" class=\"mejs-volume-slider\" aria-label=\"Volume Slider\" aria-valuemin=\"0\" aria-valuemax=\"100\" role=\"slider\" aria-orientation=\"vertical\"><span class=\"mejs-offscreen\">Use Up\/Down Arrow keys to increase or decrease volume.<\/span><\/p>\n<div class=\"mejs-volume-total\">\n<div class=\"mejs-volume-current\" style=\"bottom: 0px; height: 100%;\"><\/div>\n<div class=\"mejs-volume-handle\" style=\"bottom: 100%; margin-bottom: -3px;\"><\/div>\n<\/div>\n<p><\/a><\/div>\n<div class=\"mejs-button mejs-fullscreen-button\"><button type=\"button\" aria-controls=\"mep_0\" title=\"Fullscreen\" aria-label=\"Fullscreen\" tabindex=\"0\"><\/button><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div dir=\"ltr\" style=\"text-align: left\">\n<h3><span style=\"vertical-align: inherit\">Installation Guide<\/span><\/h3>\n<p><span style=\"vertical-align: inherit\">After Extract, view with your favorite player.<\/span><\/p>\n<p><span style=\"vertical-align: inherit\">Subtitles: None<\/span><\/p>\n<p><span style=\"vertical-align: inherit\">Quality: 1080p<\/span><\/p>\n<\/div>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Download link<\/span><\/h3>\n<p dir=\"ltr\" style=\"text-align: left\"><a href=\"https:\/\/dl2.downloadly.ir\/Files\/Elearning\/Oreilly_TensorFlow_in_Action_Video_Edition_2022-10.part1_Downloadly.ir.rar?nocache=1786113603\"><span style=\"vertical-align: inherit\">Download Part 1 \u2013 1 GB<\/span><\/a><\/p>\n<p dir=\"ltr\" style=\"text-align: left\"><a href=\"https:\/\/dl2.downloadly.ir\/Files\/Elearning\/Oreilly_TensorFlow_in_Action_Video_Edition_2022-10.part2_Downloadly.ir.rar?nocache=1786113603\"><span style=\"vertical-align: inherit\">Download Part 2 \u2013 1 GB<\/span><\/a><\/p>\n<p dir=\"ltr\" style=\"text-align: left\"><a href=\"https:\/\/dl2.downloadly.ir\/Files\/Elearning\/Oreilly_TensorFlow_in_Action_Video_Edition_2022-10.part3_Downloadly.ir.rar?nocache=1786113603\"><span style=\"vertical-align: inherit\">Download Part 3 \u2013 682 MB<\/span><\/a><\/p>\n<p dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">File(s) password: www.downloadly.ir<\/span><\/p>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">File size<\/span><\/h3>\n<p dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">2.6 GB<\/span><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Description TensorFlow in Action, Video Edition. This course teaches you the secrets of designing with TensorFlow to build successful deep learning applications<\/p>\n","protected":false},"author":1,"template":"","dgi_category":[10458],"dgi_tag":[77763,77764,77765,77766,77767,77768],"class_list":["post-8893","digital_item","type-digital_item","status-publish","has-post-thumbnail","hentry","dgi_category-video-tutorials","dgi_tag-course-tensorflow-in-action-video-edition","dgi_tag-download-course-tensorflow-in-action-video-edition","dgi_tag-download-tensorflow-in-action-video-edition","dgi_tag-free-download-tensorflow-in-action-video-edition","dgi_tag-free-tensorflow-in-action-video-edition","dgi_tag-thushan-ganegedara"],"_links":{"self":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item\/8893","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item"}],"about":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/types\/digital_item"}],"author":[{"embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"version-history":[{"count":0,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item\/8893\/revisions"}],"wp:attachment":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/media?parent=8893"}],"wp:term":[{"taxonomy":"dgi_category","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_category?post=8893"},{"taxonomy":"dgi_tag","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_tag?post=8893"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}