{"id":8503,"date":"2026-08-10T09:56:46","date_gmt":"2026-08-10T09:56:46","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/oreilly-optimization-algorithms-video-edition-2024-10\/"},"modified":"2026-08-10T09:56:46","modified_gmt":"2026-08-10T09:56:46","slug":"oreilly-optimization-algorithms-video-edition-2024-10","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/oreilly-optimization-algorithms-video-edition-2024-10\/","title":{"rendered":"Oreilly \u2013 Optimization Algorithms, Video Edition 2024-10"},"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=\"5:1-5:307\"><span style=\"vertical-align: inherit\">Optimization Algorithms, Video Edition. This comprehensive course teaches you how to use artificial intelligence algorithms to solve complex design, planning, and control problems. Whether you want to find the fastest route, determine the best price for your product, or optimize your resources, this course gives you the tools you need to solve these challenges. This course is perfect for those who are interested in learning complex concepts in a simple and practical way.<\/span><\/p>\n<h3 dir=\"ltr\" style=\"text-align: left\" data-sourcepos=\"7:1-7:25\"><span style=\"vertical-align: inherit\">What you will learn:<\/span><\/h3>\n<ul dir=\"ltr\" style=\"text-align: left\" data-sourcepos=\"9:1-18:0\">\n<li data-sourcepos=\"9:1-9:81\"><span style=\"vertical-align: inherit\">Basic Search and Optimization Concepts: Deep understanding of the fundamental concepts of search and optimization<\/span><\/li>\n<li data-sourcepos=\"10:1-10:90\"><span style=\"vertical-align: inherit\">Deterministic and stochastic optimization techniques: Introduction to different methods for solving optimization problems.<\/span><\/li>\n<li data-sourcepos=\"11:1-11:69\"><span style=\"vertical-align: inherit\">Graph Search Algorithms: Learning Graph Search Algorithms<\/span><\/li>\n<li data-sourcepos=\"12:1-12:98\"><span style=\"vertical-align: inherit\">Path-based optimization algorithms: Introduction to algorithms that seek the best path<\/span><\/li>\n<li data-sourcepos=\"13:1-13:63\"><span style=\"vertical-align: inherit\">Evolutionary Computing: Learning Algorithms Inspired by Evolution<\/span><\/li>\n<li data-sourcepos=\"14:1-14:72\"><span style=\"vertical-align: inherit\">Crowd Intelligence: Introducing Algorithms Inspired by Collective Behavior<\/span><\/li>\n<li data-sourcepos=\"15:1-15:94\"><span style=\"vertical-align: inherit\">Machine Learning for Optimization: Using Machine Learning Methods to Solve Optimization Problems<\/span><\/li>\n<li data-sourcepos=\"16:1-16:108\"><span style=\"vertical-align: inherit\">Balancing exploration and exploitation: Finding the right balance between exploring the problem space and using existing information<\/span><\/li>\n<li data-sourcepos=\"17:1-18:0\"><span style=\"vertical-align: inherit\">Python Libraries: Learn Python libraries for implementing optimization algorithms.<\/span><\/li>\n<\/ul>\n<h3 dir=\"ltr\" style=\"text-align: left\" data-sourcepos=\"19:1-19:31\"><span style=\"vertical-align: inherit\">This course is suitable for people who:<\/span><\/h3>\n<ul dir=\"ltr\" style=\"text-align: left\" data-sourcepos=\"21:1-25:0\">\n<li data-sourcepos=\"21:1-21:70\"><span style=\"vertical-align: inherit\">Are looking to learn modern AI techniques to solve real-world problems<\/span><\/li>\n<li data-sourcepos=\"22:1-22:58\"><span style=\"vertical-align: inherit\">They want to work in the field of design, planning, and control.<\/span><\/li>\n<li data-sourcepos=\"23:1-23:52\"><span style=\"vertical-align: inherit\">Are interested in learning optimization algorithms<\/span><\/li>\n<li data-sourcepos=\"24:1-25:0\"><span style=\"vertical-align: inherit\">Want to use Python\u2019s powerful tools to implement algorithms<\/span><\/li>\n<\/ul>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Course details Optimization Algorithms, Video Edition<\/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\/optimization-algorithms-video\/9781633438835VE\/?_gl=1*hiyw7v*_ga*MTU4NjE0OTc3OC4xNzEyMjIyNDU3*_ga_092EL089CH*MTczODgyMzc3Ni4zOS4xLjE3Mzg4MjM3ODcuNDkuMC4w\" 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\/alaa-khamis\/\"><span style=\"vertical-align: inherit\">Alaa Khamis<\/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: 15 hours and 54 minutes<\/span><\/li>\n<\/ul>\n<h3 dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Course headings<\/span><\/h3>\n<ul dir=\"ltr\" style=\"text-align: left\">\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 1. Deterministic search algorithms<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Introduction to search and optimization<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Going from toy problems to the real world<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Basic ingredients of optimization problems<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Well-structured problems vs. ill-structured problems<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Search algorithms and the search dilemma<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><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. A deeper look at search and optimization<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Classifying search and optimization algorithms<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Heuristics and metaheuristics<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Nature-inspired algorithms<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><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. Blind search algorithms<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Graph search<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Graph traversal algorithms<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Shortest path algorithms<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Applying blind search to the routing problem<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><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. Informed search algorithms<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Minimum spanning tree algorithms<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Shortest path algorithms<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Applying informed search to a routing problem<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 2. Trajectory-based algorithms<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Simulated annealing<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. The simulated annealing algorithm<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Function optimization<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Solving Sudoku<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Solving TSP<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Solving a delivery semi-truck routing problem<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Taboo search<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Tabu search algorithm<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Solving constraint satisfaction problems<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Solving continuous problems<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Solving TSP and routing problems<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Assembly line balancing problem<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 3. Evolutionary computing algorithms<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Genetic algorithms<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Introducing evolutionary computation<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Genetic algorithm building blocks<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Implementing genetic algorithms in Python<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><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. Genetic algorithm variants<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Real-valued GA<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Permutation-based GA<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Multi-objective optimization<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Adaptive GA<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Solving the traveling salesman problem<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. PID tuning problem<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Political districting problem<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 4. Swarm intelligence algorithms<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Particle swarm optimization<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Continuous PSO<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Binary PSO<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Permutation-based PSO<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Adaptive PSO<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Solving the traveling salesman problem<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Neural network training using PSO<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><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. Other swarm intelligence algorithms to explore<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. ACO metaheuristics<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. ACO variants<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. From hive to optimization<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Exploring the artificial bee colony algorithm<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 5. Machine learning-based methods<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Supervised and unsupervised learning<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Demystifying machine learning<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Machine learning with graphs<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Self-organizing maps<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Machine learning for optimization problems<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Solving function optimization using supervised machine learning<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Solving TSP using supervised graph machine learning<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Solving TSP using unsupervised machine learning<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Finding a convex hull<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><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. Reinforcement learning<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 12. Optimization with reinforcement learning<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 12. Balancing CartPole using A2C and PPO<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 12. Autonomous coordination in mobile networks using PPO<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 12. Solving the truck selection problem using contextual bandits<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 12. Journey\u2019s end: A final reflection<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 12. Summary<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix A. Search and optimization libraries in Python<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix A. Mathematical programming solvers<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix A. Graph and mapping libraries<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix A. Metaheuristics optimization libraries<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix A. Machine learning libraries<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix A. Projects<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix B. Benchmarks and datasets<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix B. Combinatorial optimization benchmark datasets<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix B. Geospatial datasets<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix B. Machine learning datasets<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix B. Data folder<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix C. Exercises and solutions<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix C. Chapter 3: Blind search algorithms<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix C. Chapter 4: Informed search algorithms<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix C. Chapter 5: Simulated annealing<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix C. Chapter 6: Tabu search<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix C. Chapter 7: Genetic algorithm<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix C. Chapter 8: Genetic algorithm variants<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix C. Chapter 9: Particle swarm optimization<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix C. Chapter 10: Other swarm intelligence algorithms to explore<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix C. Chapter 11: Supervised and unsupervised learning<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Appendix C. Chapter 12: Reinforcement learning<\/span><\/li>\n<\/ul>\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-961611 size-full\" src=\"https:\/\/downloadly.ir\/wp-content\/uploads\/2025\/02\/Optimization-Algorithms-Video-Edition.png\" alt=\"Optimization Algorithms, Video Edition\" width=\"1246\" height=\"436\"><\/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-153839-1\"><video class=\"wp-video-shortcode\" id=\"video-153839-1_html5\" width=\"640\" height=\"360\" preload=\"metadata\" src=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Optimization_Algorithms_Video_Edition_Downloadly.ir.mp4?_=1\" style=\"width: 640px; height: 360px;\"><source type=\"video\/mp4\" src=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Optimization_Algorithms_Video_Edition_Downloadly.ir.mp4?_=1\"><a href=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Optimization_Algorithms_Video_Edition_Downloadly.ir.mp4?nocache=1786112466774\">https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Optimization_Algorithms_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:\/\/dl1.downloadly.ir\/Files\/Elearning\/Oreilly_Optimization_Algorithms_Video_Edition_2024-10.part1_Downloadly.ir.rar?nocache=1786112466\"><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:\/\/dl1.downloadly.ir\/Files\/Elearning\/Oreilly_Optimization_Algorithms_Video_Edition_2024-10.part2_Downloadly.ir.rar?nocache=1786112466\"><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:\/\/dl1.downloadly.ir\/Files\/Elearning\/Oreilly_Optimization_Algorithms_Video_Edition_2024-10.part3_Downloadly.ir.rar?nocache=1786112466\"><span style=\"vertical-align: inherit\">Download Part 3 \u2013 588 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\">2.5 GB<\/span><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Description Optimization Algorithms, Video Edition. This comprehensive course teaches you how to use artificial intelligence algorithms to solve complex design,<\/p>\n","protected":false},"author":1,"template":"","dgi_category":[10458],"dgi_tag":[75085,75086,75087,75088,75089,75090],"class_list":["post-8503","digital_item","type-digital_item","status-publish","has-post-thumbnail","hentry","dgi_category-video-tutorials","dgi_tag-alaa-khamis","dgi_tag-course-optimization-algorithms-video-edition","dgi_tag-download-course-optimization-algorithms-video-edition","dgi_tag-download-optimization-algorithms-video-edition","dgi_tag-free-download-optimization-algorithms-video-edition","dgi_tag-free-optimization-algorithms-video-edition"],"_links":{"self":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item\/8503","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\/8503\/revisions"}],"wp:attachment":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/media?parent=8503"}],"wp:term":[{"taxonomy":"dgi_category","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_category?post=8503"},{"taxonomy":"dgi_tag","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_tag?post=8503"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}