{"id":456,"date":"2020-09-06T12:44:48","date_gmt":"2020-09-06T12:44:48","guid":{"rendered":"https:\/\/aaidm.buas.nl\/?page_id=456"},"modified":"2020-09-06T16:26:31","modified_gmt":"2020-09-06T16:26:31","slug":"ai1-preparation-for-week-7-genetic-algorithms","status":"publish","type":"page","link":"https:\/\/aaidm.buas.nl\/?page_id=456","title":{"rendered":"AI1- Preparation for Week 7 &#8211; Genetic Algorithms"},"content":{"rendered":"\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-flow wp-block-group-is-layout-flow\">\n<figure class=\"wp-block-table is-style-regular\"><table><tbody><tr><td>Author:<\/td><td><a href=\"https:\/\/francescolelli.info\/\" target=\"_blank\" rel=\"noreferrer noopener\">Francesco Lelli<\/a><\/td><\/tr><tr><td>Learning Line:<\/td><td>Genetic Algorithms<\/td><\/tr><tr><td>Course:<\/td><td>AI1: Introduction to Neural Networks<\/td><\/tr><tr><td>Week:<\/td><td>7 <\/td><\/tr><tr><td>Competencies:<\/td><td>\u00ad Understand and be able to use neural network dynamics and factors that influence their performance<\/td><\/tr><tr><td>BoKS:<\/td><td>3aS4 The student is able to distinguish supervised, unsupervised and reinforced learning Machine Learning, and is able to develop specific learning strategies for specific business contexts.<\/td><\/tr><tr><td>Learning Goals:<\/td><td>Genetic Algorithms and more practical aspects <\/td><\/tr><\/tbody><\/table><\/figure>\n<\/div><\/div>\n\n\n\n<p><strong>Artificial neural networks<\/strong>&nbsp;(<strong>ANN<\/strong>) or simply \u201cneural networks\u201d are computing systems inspired by \u201cclassical biological\u201d neural networks that are in animal brains.&nbsp;Typically&nbsp;an ANN \u201clearn\u201d to perform tasks by considering examples, without being programmed with task-specific rules.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"672\" height=\"440\" src=\"https:\/\/aaidm.buas.nl\/wp-content\/uploads\/2020\/06\/image002.jpg\" alt=\"\" class=\"wp-image-71\" srcset=\"https:\/\/aaidm.buas.nl\/wp-content\/uploads\/2020\/06\/image002.jpg 672w, https:\/\/aaidm.buas.nl\/wp-content\/uploads\/2020\/06\/image002-300x196.jpg 300w\" sizes=\"auto, (max-width: 672px) 100vw, 672px\" \/><\/figure>\n\n\n\n<p>By now you should be able to implement the &#8220;hello world&#8221; of Neural Networks. In thinking to what is missing in these lectures I would say that an introduction to genetic algorithms is probably the most important part. In a nutshell is a technique for optimizing a particular model that is inspired by biological evolution theories. When we design a neural network we usually make a lot of assumption on its characteristics such as, for example, type of activation of the neurons, hidden layers, type of connections, way of back-propagate the feedback etc. As these aspects could be considered features of a particular network, a genetic algorithm approach could help in finding the optimal configuration of these parameters.\u00a0<\/p>\n\n\n\n<p>This video introduce genetic algorithms that we will see in the coming lecture<\/p>\n\n\n\n<figure class=\"wp-block-embed-youtube wp-block-embed is-type-video is-provider-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Evolutionary Algorithms\" width=\"640\" height=\"360\" src=\"https:\/\/www.youtube.com\/embed\/L--IxUH4fac?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<p>More details are available in the following book: <\/p>\n\n\n\n<ul class=\"wp-block-list\"><li><a href=\"http:\/\/neuralnetworksanddeeplearning.com\/\">http:\/\/neuralnetworksanddeeplearning.com\/<\/a>\u00a0<\/li><\/ul>\n\n\n\n<p>A second aspect that is missing is the notion of \u201cdeep learning\u201d that is a particular way of dealing with\u00a0backprogation\u00a0of a neural network without the need of having a train dataset and using a larger set of layers.\u00a0This will be material for the coming classes. <\/p>\n\n\n\n<p>This is week 7 of AI1: Introduction to Neural Networks, link to the all material of the course: <\/p>\n\n\n\n<ul class=\"wp-block-list\"><li><a href=\"http:\/\/AI1: Introduction to Artificial Intelligence\" target=\"_blank\" rel=\"noreferrer noopener\">AI1: Introduction to Neural Networks<\/a><\/li><\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Author: Francesco Lelli Learning Line: Genetic Algorithms Course: AI1: Introduction to Neural Networks Week: 7 Competencies: \u00ad Understand and be able to use neural network dynamics and factors that influence their performance BoKS: 3aS4 The student is able to distinguish supervised, unsupervised and reinforced learning Machine Learning, and is able to develop specific learning strategies [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"parent":87,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-456","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.6 - 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