{"id":9959,"date":"2026-08-10T10:13:21","date_gmt":"2026-08-10T10:13:21","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-mathematics-behind-backpropagation-theory-and-python-code-2025-1\/"},"modified":"2026-08-10T10:13:21","modified_gmt":"2026-08-10T10:13:21","slug":"udemy-mathematics-behind-backpropagation-theory-and-python-code-2025-1","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-mathematics-behind-backpropagation-theory-and-python-code-2025-1\/","title":{"rendered":"Udemy \u2013 Mathematics Behind Backpropagation | Theory and Python Code 2025-1"},"content":{"rendered":"<div class=\"w-post-elm post_content\">\n<h2><span style=\"vertical-align: inherit\">Description<\/span><\/h2>\n<p><span style=\"vertical-align: inherit\">The Mathematics Behind Backpropagation | Theory and Python Code course explores the secrets of the algorithm that powers modern artificial intelligence: backpropagation. This fundamental concept drives the learning process in neural networks and enables technologies like self-driving cars, large language models, advances in medical imaging, and much more. This course takes students on a journey from beginner to mastery, exploring backpropagation from both theory and practical implementation. Starting with the basics, the course teaches the mathematics behind backpropagation, including derivatives, partial derivatives, and gradients. It also explains the concept of gradient descent in simple terms and shows how to efficiently optimize machine performance.<\/span><\/p>\n<div data-purpose=\"safely-set-inner-html:description:description\">\n<p data-sourcepos=\"11:1-11:482\"><span style=\"vertical-align: inherit\">But this course is not just about theory; participants will dive into the practical side of things and implement post-propagation from scratch. At first, all calculations are done manually to gain a deep understanding of each step. Then, they will move on to Python programming and build their own neural network without using any pre-built libraries or tools. By the end of this course, graduates will have a complete understanding of how post-propagation works, from the math to the code and beyond. Whether you are an aspiring machine learning engineer, a software developer entering the world of AI, or a data scientist looking for a deeper understanding, this course will equip you with rare skills that most professionals lack. Stand out in the AI \u200b\u200bfield by mastering post-propagation and gain the confidence to build neural networks with the foundational knowledge that will set you apart in this competitive arena.<\/span><\/p>\n<h3 data-sourcepos=\"15:1-15:24\"><strong><span style=\"vertical-align: inherit\">What you will learn<\/span><\/strong><\/h3>\n<ul data-sourcepos=\"17:1-31:0\">\n<li data-sourcepos=\"17:1-17:43\"><span style=\"vertical-align: inherit\">Understanding and implementing manual and post-release coding<\/span><\/li>\n<li data-sourcepos=\"18:1-18:34\"><span style=\"vertical-align: inherit\">Understanding the mathematical foundations of neural networks<\/span><\/li>\n<li data-sourcepos=\"19:1-19:78\"><span style=\"vertical-align: inherit\">Build and train your own feedforward neural network in Python without using any libraries<\/span><\/li>\n<li data-sourcepos=\"20:1-20:32\"><span style=\"vertical-align: inherit\">Review of common problems in post-release<\/span><\/li>\n<li data-sourcepos=\"21:1-21:62\"><span style=\"vertical-align: inherit\">Numerical calculation of derivatives, partial derivatives, and gradients through examples<\/span><\/li>\n<li data-sourcepos=\"22:1-22:43\"><span style=\"vertical-align: inherit\">Finding derivatives of loss functions and activation functions<\/span><\/li>\n<li data-sourcepos=\"23:1-23:18\"><span style=\"vertical-align: inherit\">Understanding the concept of derivatives<\/span><\/li>\n<li data-sourcepos=\"24:1-24:41\"><span style=\"vertical-align: inherit\">Visually observe the gradient descent function<\/span><\/li>\n<li data-sourcepos=\"25:1-25:30\"><span style=\"vertical-align: inherit\">Manual implementation of gradient descent<\/span><\/li>\n<li data-sourcepos=\"26:1-26:48\"><span style=\"vertical-align: inherit\">Using Python to code multiple neural networks<\/span><\/li>\n<li data-sourcepos=\"27:1-27:42\"><span style=\"vertical-align: inherit\">Understanding how partial derivatives work in post-diffusion<\/span><\/li>\n<li data-sourcepos=\"28:1-28:62\"><span style=\"vertical-align: inherit\">Understanding gradients and how they guide machines to learn<\/span><\/li>\n<li data-sourcepos=\"29:1-29:41\"><span style=\"vertical-align: inherit\">Learn why activation functions are used<\/span><\/li>\n<li data-sourcepos=\"30:1-31:0\"><span style=\"vertical-align: inherit\">Understanding the role of learning rate in gradient descent<\/span><\/li>\n<\/ul>\n<h3 data-sourcepos=\"32:1-32:30\"><strong><span style=\"vertical-align: inherit\">This course is suitable for people who:<\/span><\/strong><\/h3>\n<ul data-sourcepos=\"34:1-38:149\">\n<li data-sourcepos=\"34:1-34:91\"><span style=\"vertical-align: inherit\">Data scientists who want to gain a deeper understanding of the mathematical foundations of neural networks.<\/span><\/li>\n<li data-sourcepos=\"35:1-35:100\"><span style=\"vertical-align: inherit\">Passionate machine learning engineers who want to build a strong foundation in the algorithms that power AI.<\/span><\/li>\n<li data-sourcepos=\"36:1-36:98\"><span style=\"vertical-align: inherit\">Software developers looking to enter the exciting world of machine learning and artificial intelligence.<\/span><\/li>\n<li data-sourcepos=\"37:1-37:94\"><span style=\"vertical-align: inherit\">Students and enthusiasts who are eager to learn how machine learning actually works behind the scenes.<\/span><\/li>\n<li data-sourcepos=\"38:1-38:149\"><span style=\"vertical-align: inherit\">Professionals who strive to remain competitive in the era of large language models and advanced artificial intelligence by mastering skills beyond basic frameworks.<\/span><\/li>\n<\/ul>\n<\/div>\n<h3><span style=\"vertical-align: inherit\">Course details Mathematics Behind Backpropagation | Theory and Python Code<\/span><\/h3>\n<ul>\n<li><span style=\"vertical-align: inherit\">Publisher:&nbsp; <\/span><a href=\"https:\/\/href.li\/?https:\/\/www.udemy.com\/course\/mathematics-behind-backpropagation-theory-and-python-code\/?couponCode=LEARNNOWPLANSGB\" target=\"_blank\" rel=\"noopener\"><span style=\"vertical-align: inherit\">Udemy<\/span><\/a><\/li>\n<li><span style=\"vertical-align: inherit\">Instructor:&nbsp; <\/span><a class=\"ud-btn ud-btn-medium ud-btn-link ud-heading-sm ud-text-sm ud-instructor-links\" href=\"https:\/\/downloadlynet.ir\/tag\/patrik-szepesi\/\" data-position=\"1\"><span class=\"ud-btn-label\"><span style=\"vertical-align: inherit\">Patrik Szepesi<\/span><\/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: 4 hours and 37 minutes<\/span><\/li>\n<li><span style=\"vertical-align: inherit\">Number of lessons: 40<\/span><\/li>\n<\/ul>\n<h3><span style=\"vertical-align: inherit\">Course headings<\/span><\/h3>\n<h3><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-979992 size-full\" src=\"https:\/\/downloadly.ir\/wp-content\/uploads\/2025\/04\/Mathematics-Behind-Backpropagation-Theory-and-Python-Code12.png\" alt=\"Mathematics Behind Backpropagation | Theory and Python Code\" width=\"802\" height=\"338\"><\/h3>\n<h3><span style=\"vertical-align: inherit\">Prerequisites for the Mathematics Behind Backpropagation | Theory and Python Code course<\/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=\"ud-block-list-item-content\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">basic python knowledge<\/span><\/li>\n<li class=\"ud-block-list-item-content\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">high school mathematics<\/span><\/li>\n<\/ul>\n<\/div>\n<h3><span style=\"vertical-align: inherit\">Course images<\/span><\/h3>\n<h2><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-979991 size-full\" src=\"https:\/\/downloadly.ir\/wp-content\/uploads\/2025\/04\/Mathematics-Behind-Backpropagation-Theory-and-Python-Code.png\" alt=\"Mathematics Behind Backpropagation | Theory and Python Code\" width=\"783\" height=\"387\"><\/h2>\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-162559-1\"><video class=\"wp-video-shortcode\" id=\"video-162559-1_html5\" width=\"640\" height=\"360\" preload=\"metadata\" src=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Mathematics_Behind_Backpropagation__Theory_and_Python_Code_Downloadly.ir.mp4?_=1\" style=\"width: 640px; height: 360px;\"><source type=\"video\/mp4\" src=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Mathematics_Behind_Backpropagation__Theory_and_Python_Code_Downloadly.ir.mp4?_=1\"><a href=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Mathematics_Behind_Backpropagation__Theory_and_Python_Code_Downloadly.ir.mp4?nocache=1786136756787\">https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Mathematics_Behind_Backpropagation__Theory_and_Python_Code_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>\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:\/\/dl3.downloadly.ir\/Files\/Elearning\/Udemy_Mathematics_Behind_Backpropagation_Theory_and_Python_Code_2025-1.part1_Downloadly.ir.rar?nocache=1786136756\"><span style=\"vertical-align: inherit\">Download Part 1 \u2013 1 GB<\/span><\/a><\/p>\n<p><a href=\"https:\/\/dl3.downloadly.ir\/Files\/Elearning\/Udemy_Mathematics_Behind_Backpropagation_Theory_and_Python_Code_2025-1.part2_Downloadly.ir.rar?nocache=1786136756\"><span style=\"vertical-align: inherit\">Download Part 2 \u2013 79 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\">1.07 GB<\/span><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Description The Mathematics Behind Backpropagation | Theory and Python Code course explores the secrets of the algorithm that powers modern artificial intellige<\/p>\n","protected":false},"author":1,"template":"","dgi_category":[10458],"dgi_tag":[85060,85061,85062,85063,85064,67231],"class_list":["post-9959","digital_item","type-digital_item","status-publish","has-post-thumbnail","hentry","dgi_category-video-tutorials","dgi_tag-course-mathematics-behind-backpropagation-theory-and-python-code","dgi_tag-download-course-mathematics-behind-backpropagation-theory-and-python-code","dgi_tag-download-mathematics-behind-backpropagation-theory-and-python-code","dgi_tag-free-download-mathematics-behind-backpropagation-theory-and-python-code","dgi_tag-free-mathematics-behind-backpropagation-theory-and-python-code","dgi_tag-patrik-szepesi"],"_links":{"self":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item\/9959","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\/9959\/revisions"}],"wp:attachment":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/media?parent=9959"}],"wp:term":[{"taxonomy":"dgi_category","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_category?post=9959"},{"taxonomy":"dgi_tag","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_tag?post=9959"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}