{"id":8504,"date":"2026-08-10T09:56:47","date_gmt":"2026-08-10T09:56:47","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/oreilly-evolutionary-deep-learning-video-edition-2023-8\/"},"modified":"2026-08-10T09:56:47","modified_gmt":"2026-08-10T09:56:47","slug":"oreilly-evolutionary-deep-learning-video-edition-2023-8","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/oreilly-evolutionary-deep-learning-video-edition-2023-8\/","title":{"rendered":"Oreilly \u2013 Evolutionary Deep Learning, Video Edition 2023-8"},"content":{"rendered":"<div class=\"w-post-elm post_content\">\n<h2><span style=\"vertical-align: inherit\">Description<\/span><\/h2>\n<p data-sourcepos=\"5:1-6:260\"><span style=\"vertical-align: inherit\">Evolutionary Deep Learning Video Edition. In this edition, the narrator reads the book while the content, graphs, code, and text are displayed on the screen. This course is like an audiobook that you can also watch as a video. Evolutionary Deep Learning is your guide to improving deep learning models using AutoML enhancements based on the principles of biological evolution. This exciting new approach uses lesser-known AI approaches to increase performance without hours of data labeling or model hyperparameter tuning. In this unique guide, you\u2019ll discover tools to optimize everything from data collection to your network architecture.<\/span><\/p>\n<h3 data-sourcepos=\"8:1-9:49\"><strong><span style=\"vertical-align: inherit\">What you will learn:<\/span><\/strong><\/h3>\n<ul data-sourcepos=\"10:1-17:0\">\n<li data-sourcepos=\"10:1-10:71\"><span style=\"vertical-align: inherit\">Solve complex design and analysis problems using evolutionary computation.<\/span><\/li>\n<li data-sourcepos=\"11:1-11:119\"><span style=\"vertical-align: inherit\">Tune deep learning hyperparameters using evolutionary computation, genetic algorithms, and particle swarm optimization.<\/span><\/li>\n<li data-sourcepos=\"12:1-12:95\"><span style=\"vertical-align: inherit\">Reproduce sample data using a deep learning autoencoder in unsupervised learning.<\/span><\/li>\n<li data-sourcepos=\"13:1-13:54\"><span style=\"vertical-align: inherit\">Understand the principles of reinforcement learning and the Q-Learning equation.<\/span><\/li>\n<li data-sourcepos=\"14:1-14:77\"><span style=\"vertical-align: inherit\">Apply Q-Learning to deep learning to create deep reinforcement learning.<\/span><\/li>\n<li data-sourcepos=\"15:1-15:62\"><span style=\"vertical-align: inherit\">Optimize the loss function and architecture of the unsupervised self-encoding network.<\/span><\/li>\n<li data-sourcepos=\"16:1-17:0\"><span style=\"vertical-align: inherit\">Create an evolutionary agent that can play an OpenAI Gym game.<\/span><\/li>\n<\/ul>\n<h3 data-sourcepos=\"20:1-21:68\"><strong><span style=\"vertical-align: inherit\">This course is suitable for people who:<\/span><\/strong><\/h3>\n<ul>\n<li data-sourcepos=\"20:1-21:68\"><span style=\"vertical-align: inherit\">This course is suitable for data scientists who are familiar with Python.<\/span><\/li>\n<\/ul>\n<h3><span style=\"vertical-align: inherit\">Evolutionary Deep Learning Video Edition Course Specifications<\/span><\/h3>\n<ul>\n<li><span style=\"vertical-align: inherit\">Publisher: <\/span><a href=\"https:\/\/href.li\/?https:\/\/www.oreilly.com\/library\/view\/evolutionary-deep-learning\/9781617299520VE\/\/\" target=\"_blank\" rel=\"noopener\"><span style=\"vertical-align: inherit\">Oreilly<\/span><\/a><\/li>\n<li><span style=\"vertical-align: inherit\">Instructor: <\/span><a href=\"https:\/\/downloadlynet.ir\/tag\/micheal-lanham\/\"><span style=\"vertical-align: inherit\">Michael Lanham<\/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: 9 hours and 39 minutes<\/span><\/li>\n<\/ul>\n<h3><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\">\n<ul>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 1. Getting started<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Introducing evolutionary deep learning<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. The why and where of evolutionary deep learning<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. The need for deep learning optimization<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Automating optimization with automated machine learning<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Applications of evolutionary deep learning<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Summary<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Introducing evolutionary computation<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Simulating life with Python<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Life simulation as optimization<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Adding evolution to the life simulation<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Genetic algorithms in Python<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Summary<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Introducing genetic algorithms with DEAP<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Solving the Queen\u2019s Gambit<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Helping a traveling salesman<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Selecting genetic operators for improved evolution<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Painting with the EvoLisa<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Summary<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. More evolutionary computation with DEAP<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Particle swarm optimization with DEAP<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Coevolving solutions with DEAP<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Evolutionary strategies with DEAP<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Differential evolution with DEAP<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Summary<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 2. Optimizing deep learning<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Automating hyperparameter optimization<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Automating HPO with random search<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Grid search and HPO<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Evolutionary computation for HPO<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Genetic algorithms and evolutionary strategies for HPO<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Differential evolution for HPO<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Summary<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Neuroevolution optimization<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Genetic algorithms as deep learning optimizers<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Other evolutionary methods for neurooptimization<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Applying neuroevolution optimization to Keras<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Understanding the limits of evolutionary optimization<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Summary<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Evolutionary convolutional neural networks<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Encoding a network architecture in genes<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Creating the mating crossover operation<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Developing a custom mutation operator<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Evolving convolutional network architecture<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Summary<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 3. Advanced applications<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Evolving autoencoders<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Evolutionary AE optimization<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Mating and mutating the autoencoder gene sequence<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Evolving an autoencoder<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Building variational autoencoders<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Summary<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Generative deep learning and evolution<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. The challenges of training a GAN<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Fixing GAN problems with Wasserstein loss<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Encoding the Wasserstein DCGAN for evolution<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Optimizing the DCGAN with genetic algorithms<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Summary<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. NEAT: NeuroEvolution of Augmenting Topologies<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Visualizing an evolved NEAT network<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Exercising the capabilities of NEAT<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Exercising NEAT to classify images<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Uncovering the role of speciation in evolving topologies<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Summary<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Evolutionary learning with NEAT<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Exploring complex problems from the OpenAI Gym<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Solving reinforcement learning problems with NEAT<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Solving Gym\u2019s lunar lander problem with NEAT agents<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Solving Gym\u2019s lunar lander problem with a deep Q-network<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Summary<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 12. Evolutionary machine learning and beyond<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 12. Revisiting reinforcement learning with Geppy<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 12. Introducing instinctual learning<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 12. Generalized learning with genetic programming<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 12. The future of evolutionary machine learning<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 12. Generalization with deep instinctual and deep reinforcement learning<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 12. Summary<\/span><\/li>\n<\/ul>\n<\/div>\n<h3><span style=\"vertical-align: inherit\">Course images<\/span><\/h3>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-961840 size-full\" src=\"https:\/\/downloadly.ir\/wp-content\/uploads\/2025\/02\/Evolutionary-Deep-Learning-Video-Edition.png\" alt=\"Evolutionary Deep Learning Video Edition\" width=\"966\" height=\"356\"><\/p>\n<h3><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-153847-1\"><video class=\"wp-video-shortcode\" id=\"video-153847-1_html5\" width=\"640\" height=\"360\" preload=\"metadata\" src=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Evolutionary_Deep_Learning_Video_Edition_Downloadly.ir.mp4?_=1\" style=\"width: 640px; 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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>\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: 720p<\/span><\/p>\n<\/div>\n<h3><span style=\"vertical-align: inherit\">Download link<\/span><\/h3>\n<p><a href=\"https:\/\/dl1.downloadly.ir\/Files\/Elearning\/Oreilly_Evolutionary_Deep_Learning_Video_Edition_2023-8.part1_Downloadly.ir.rar?nocache=1786112467\"><span style=\"vertical-align: inherit\">Download Part 1 \u2013 1 GB<\/span><\/a><\/p>\n<p><a href=\"https:\/\/dl1.downloadly.ir\/Files\/Elearning\/Oreilly_Evolutionary_Deep_Learning_Video_Edition_2023-8.part2_Downloadly.ir.rar?nocache=1786112467\"><span style=\"vertical-align: inherit\">Download Part 2 \u2013 198 MB<\/span><\/a><\/p>\n<p><span style=\"vertical-align: inherit\">File(s) password: www.downloadly.ir<\/span><\/p>\n<h3><span style=\"vertical-align: inherit\">File size<\/span><\/h3>\n<p><span style=\"vertical-align: inherit\">1.1 GB<\/span><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Description Evolutionary Deep Learning Video Edition. In this edition, the narrator reads the book while the content, graphs, code, and text are displayed on th<\/p>\n","protected":false},"author":1,"template":"","dgi_category":[10458],"dgi_tag":[75091,75092,75093,75094,75095,75096],"class_list":["post-8504","digital_item","type-digital_item","status-publish","has-post-thumbnail","hentry","dgi_category-video-tutorials","dgi_tag-course-evolutionary-deep-learning-video-edition","dgi_tag-download-course-evolutionary-deep-learning-video-edition","dgi_tag-download-evolutionary-deep-learning-video-edition","dgi_tag-free-download-evolutionary-deep-learning-video-edition","dgi_tag-free-evolutionary-deep-learning-video-edition","dgi_tag-micheal-lanham"],"_links":{"self":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item\/8504","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\/8504\/revisions"}],"wp:attachment":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/media?parent=8504"}],"wp:term":[{"taxonomy":"dgi_category","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_category?post=8504"},{"taxonomy":"dgi_tag","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_tag?post=8504"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}