{"id":195,"date":"2019-12-15T16:43:55","date_gmt":"2019-12-15T16:43:55","guid":{"rendered":"http:\/\/localhost\/wordpress\/?p=195"},"modified":"2020-03-15T23:58:31","modified_gmt":"2020-03-15T23:58:31","slug":"dimensionality-reduction-feature-extraction-techniques-in-python-part-two","status":"publish","type":"post","link":"http:\/\/localhost\/wordpress\/dimensionality-reduction-feature-extraction-techniques-in-python-part-two\/","title":{"rendered":"Dimensionality Reduction &#038; Feature Extraction Techniques in Python: Part two"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"195\" class=\"elementor elementor-195\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-32010c9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"32010c9\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0af6fdb\" data-id=\"0af6fdb\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-ef35ef5 elementor-widget elementor-widget-text-editor\" data-id=\"ef35ef5\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Part two of the Dimensionality Reduction &amp; Feature Extraction goes over the real-world application of using these techniques. Download the file by clicking <a href=\"https:\/\/gitlab.com\/jamil_projects\/jamil_machine_learning\/data_reduction\/blob\/master\/Data_reduction_overview.ipynb\">here<\/a><\/p><h2>Requirements:\u00a0<\/h2><ul><li>Anaconda Navigator \/ Jupyter notebook<\/li><li>Keras, ipywidgets, seaborn<\/li><\/ul><h2>How to use:<\/h2><ul><li>Compile the first 6 Cells<ul><li>Stop on the cell starting with the code: display<span class=\"token punctuation\">(<\/span>test_size<span class=\"token punctuation\">)<\/span><\/li><\/ul><\/li><li>The last cell should generate a bunch of User Interfaces that can allow you to set the parameters you desire<\/li><li>Click run me to run the Deep Neural Network<\/li><\/ul><p>\u00a0<\/p><p>This Jupyter notebook will give you the chance to run the same Sequential Neural Network on the iris dataset with different reduction techniques, epochs, validation\/verification split, and batch sizes. These parameters can affect the overall training time and accuracy of a neural network and I encourage you to find which set of parameters take the longest, and shortest training time!<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Part two of the Dimensionality Reduction &amp; Feature Extraction goes over the real-world application of using these techniques.<\/p>\n<p><a class=\"more-link\" href=\"http:\/\/localhost\/wordpress\/dimensionality-reduction-feature-extraction-techniques-in-python-part-two\/\">Read More<\/a><\/p>\n","protected":false},"author":1,"featured_media":153,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","footnotes":""},"categories":[15,16,17],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.5 - 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