{"id":10633,"date":"2020-10-22T10:35:11","date_gmt":"2020-10-22T10:35:11","guid":{"rendered":"https:\/\/www.experfy.com\/blog\/?p=10633"},"modified":"2023-10-17T10:58:27","modified_gmt":"2023-10-17T10:58:27","slug":"must-read-data-science-papers-how-to-use","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/must-read-data-science-papers-how-to-use\/","title":{"rendered":"5 Must-Read Data Science Papers (and How to Use Them)"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"10633\" class=\"elementor elementor-10633\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-e23b21f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e23b21f\" data-element_type=\"section\" data-e-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=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3753308\" data-id=\"3753308\" data-element_type=\"column\" data-e-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-28b67c5 elementor-widget elementor-widget-text-editor\" data-id=\"28b67c5\" data-element_type=\"widget\" data-e-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\t\t<p>Foundational ideas to keep you on top of the machine learning game.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-11f2a13 elementor-widget elementor-widget-text-editor\" data-id=\"11f2a13\" data-element_type=\"widget\" data-e-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\t\t<p>Data science might be a young field, but that doesn\u2019t mean you won\u2019t face expectations about having awareness of certain topics. This article covers several of the most important recent developments and influential thought pieces.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-46ab46a elementor-widget elementor-widget-text-editor\" data-id=\"46ab46a\" data-element_type=\"widget\" data-e-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\t\t<p>Topics covered in these papers range from the <strong>orchestration of the DS workflow<\/strong> to <strong>breakthroughs in faster neural networks<\/strong> to a <strong>rethinking of our fundamental approach to problem solving with statistics<\/strong>. For each paper, I offer ideas for how you can apply these ideas to your own work<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6cc5d99 elementor-widget elementor-widget-text-editor\" data-id=\"6cc5d99\" data-element_type=\"widget\" data-e-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\t\t<p>We\u2019ll wrap things up with a <a href=\"https:\/\/towardsdatascience.com\/must-read-data-science-papers-487cce9a2020#b51d\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\"><strong> survey<\/strong><\/a> so that you can see what the community thinks is the most important topic out of this group of papers.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-21fb15f elementor-widget elementor-widget-text-editor\" data-id=\"21fb15f\" data-element_type=\"widget\" data-e-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\t\t<p><strong>#1 \u2014<\/strong> <a href=\"https:\/\/papers.nips.cc\/paper\/5656-hidden-technical-debt-in-machine-learning-systems.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Hidden Technical Debt in Machine Learning Systems<\/strong><\/a><\/p>\t\t\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<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-23ccd94 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"23ccd94\" data-element_type=\"section\" data-e-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=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3dc4899\" data-id=\"3dc4899\" data-element_type=\"column\" data-e-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-f3b13cd elementor-widget elementor-widget-text-editor\" data-id=\"f3b13cd\" data-element_type=\"widget\" data-e-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\t\t<p>The team at Google Research provides <strong> clear instructions on antipatterns to avoid<\/strong> when setting up your data science workflow. This paper borrows the metaphor of technical debt from software engineering and applies it to data science.<\/p>\t\t\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<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-0171607 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0171607\" data-element_type=\"section\" data-e-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=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-80f0567\" data-id=\"80f0567\" data-element_type=\"column\" data-e-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-dc74b56 elementor-widget elementor-widget-image\" data-id=\"dc74b56\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"472\" src=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/1_GDTkJPIhGjWGw6rAgBSQyQ-1024x472.png\" class=\"attachment-large size-large wp-image-33500\" alt=\"\" srcset=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/1_GDTkJPIhGjWGw6rAgBSQyQ-1024x472.png 1024w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/1_GDTkJPIhGjWGw6rAgBSQyQ-300x138.png 300w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/1_GDTkJPIhGjWGw6rAgBSQyQ-768x354.png 768w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/1_GDTkJPIhGjWGw6rAgBSQyQ-1536x708.png 1536w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/1_GDTkJPIhGjWGw6rAgBSQyQ-610x281.png 610w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/1_GDTkJPIhGjWGw6rAgBSQyQ-750x345.png 750w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/1_GDTkJPIhGjWGw6rAgBSQyQ-1140x525.png 1140w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/1_GDTkJPIhGjWGw6rAgBSQyQ.png 1728w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\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<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-2b97feb elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2b97feb\" data-element_type=\"section\" data-e-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=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-393dd0e\" data-id=\"393dd0e\" data-element_type=\"column\" data-e-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-88b6c86 elementor-widget elementor-widget-text-editor\" data-id=\"88b6c86\" data-element_type=\"widget\" data-e-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\t\t<p>As the next paper explores in greater detail, building a machine learning product is a highly specialized subset of software engineering, so it makes sense that many lessons drawn from this discipline will apply to data science as well.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b4fd391 elementor-widget elementor-widget-text-editor\" data-id=\"b4fd391\" data-element_type=\"widget\" data-e-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\t\t<p><strong>How to use<\/strong>: follow the experts <a href=\"https:\/\/papers.nips.cc\/paper\/5656-hidden-technical-debt-in-machine-learning-systems.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">practical tips<\/a> to streamline development and production.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bb21f2f elementor-widget elementor-widget-text-editor\" data-id=\"bb21f2f\" data-element_type=\"widget\" data-e-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\t\t<p><b><strong>#2 \u2014<\/strong> <a href=\"https:\/\/medium.com\/@karpathy\/software-2-0-a64152b37c35\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\"><strong>Software 2.0<\/strong><\/a><\/b><\/p>\t\t\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<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-b451404 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b451404\" data-element_type=\"section\" data-e-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=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1a1b2c9\" data-id=\"1a1b2c9\" data-element_type=\"column\" data-e-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-22cea32 elementor-widget elementor-widget-text-editor\" data-id=\"22cea32\" data-element_type=\"widget\" data-e-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\t\t<p>This classic post from <a href=\"https:\/\/medium.com\/u\/ac9d9a35533e?source=post_page-----487cce9a2020--------------------------------\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">Andrej Karpathy<\/a> articulated the paradigm that machine learning models are <strong>software applications with code based in data<\/strong>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a41a034 elementor-widget elementor-widget-text-editor\" data-id=\"a41a034\" data-element_type=\"widget\" data-e-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\t\t<p>If data science is software, what exactly are we building towards? Ben Bengafort explored this question in an influential blog post called \u201c<a href=\"https:\/\/districtdatalabs.silvrback.com\/the-age-of-the-data-product\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>The Age of the Data Product<\/strong>;<\/a>.\u201d<\/p>\t\t\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<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-e0fed61 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e0fed61\" data-element_type=\"section\" data-e-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=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-77eb28d\" data-id=\"77eb28d\" data-element_type=\"column\" data-e-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-1c2f1e1 elementor-widget elementor-widget-image\" data-id=\"1c2f1e1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1024\" height=\"683\" src=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/0_JFKv38kymxR2jVvg-1024x683.jpeg\" class=\"attachment-large size-large wp-image-33501\" alt=\"\" srcset=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/0_JFKv38kymxR2jVvg-1024x683.jpeg 1024w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/0_JFKv38kymxR2jVvg-300x200.jpeg 300w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/0_JFKv38kymxR2jVvg-768x512.jpeg 768w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/0_JFKv38kymxR2jVvg-1536x1024.jpeg 1536w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/0_JFKv38kymxR2jVvg-2048x1365.jpeg 2048w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/0_JFKv38kymxR2jVvg-610x407.jpeg 610w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/0_JFKv38kymxR2jVvg-750x500.jpeg 750w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/0_JFKv38kymxR2jVvg-1140x760.jpeg 1140w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\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<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-972048b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"972048b\" data-element_type=\"section\" data-e-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=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b47aa3d\" data-id=\"b47aa3d\" data-element_type=\"column\" data-e-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-a039e57 elementor-widget elementor-widget-text-editor\" data-id=\"a039e57\" data-element_type=\"widget\" data-e-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\t\t<p><strong>How to use<\/strong>: read more about how the data product fits into the <a href=\"https:\/\/medium.com\/atlas-research\/model-selection-d190fb8bbdda\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">model selection process<\/a>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b88f459 elementor-widget elementor-widget-text-editor\" data-id=\"b88f459\" data-element_type=\"widget\" data-e-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\t\t<p><b><strong>#3 \u2014 <\/strong><a href=\"https:\/\/arxiv.org\/abs\/1810.04805\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding<\/strong><\/a><\/b><\/p>\t\t\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<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-7657976 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7657976\" data-element_type=\"section\" data-e-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=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0329777\" data-id=\"0329777\" data-element_type=\"column\" data-e-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-c42f289 elementor-widget elementor-widget-text-editor\" data-id=\"c42f289\" data-element_type=\"widget\" data-e-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\t\t<p>In this paper, the team at Google Research put forward the <a href=\"https:\/\/www.experfy.com\/blog\/natural-language-processing-and-affective-computing\/\" target=\"_blank\" rel=\"noreferrer noopener\">natural language processing<\/a> (NLP) model that represented a step-function increase in our capabilities in for text analysis.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0afc57e elementor-widget elementor-widget-text-editor\" data-id=\"0afc57e\" data-element_type=\"widget\" data-e-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\t\t<p>Though there\u2019s <a href=\"https:\/\/text-machine-lab.github.io\/blog\/2020\/bert-secrets\/\" target=\"_blank\" rel=\"noreferrer noopener\">some controversy<\/a> over exactly why BERT works so well, this is a great reminder that the machine learning field may has uncovered successful approaches without fully understanding how they work. <a href=\"https:\/\/www.youtube.com\/watch?v=B7m0e-3u-1Y&amp;amp;t=1s\" target=\"_blank\" rel=\"noreferrer noopener\">As with nature<\/a>, artificial neural networks are steeped in mystery<\/p>\t\t\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<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-864ced3 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"864ced3\" data-element_type=\"section\" data-e-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=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3a0098b\" data-id=\"3a0098b\" data-element_type=\"column\" data-e-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-f12de77 elementor-widget elementor-widget-video\" data-id=\"f12de77\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=B7m0e-3u-1Y&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\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<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-c614cf2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c614cf2\" data-element_type=\"section\" data-e-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=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0ead4c1\" data-id=\"0ead4c1\" data-element_type=\"column\" data-e-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-a6dea81 elementor-widget elementor-widget-text-editor\" data-id=\"a6dea81\" data-element_type=\"widget\" data-e-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\t\t<p><span style=\"color: #a0a0a0; font-family: ProximaNova-Regular; font-size: 11px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; text-align: center;\">In this delightful clip, the Director of Data Science at Nordstrom explains how artificialial neural nets draw inspiration from nature.<\/span><\/p>\t\t\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<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-52e4b5d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"52e4b5d\" data-element_type=\"section\" data-e-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=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-bc5e1e5\" data-id=\"bc5e1e5\" data-element_type=\"column\" data-e-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-91a2149 elementor-widget elementor-widget-text-editor\" data-id=\"91a2149\" data-element_type=\"widget\" data-e-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\t\t<p><span style=\"font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: bold; font-size: 19px; font-family: ProximaNova-Regular;\">How to Use<\/span><span style=\"font-family: ProximaNova-Regular; font-size: 19px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400;\">:<\/span><\/p>\n<ul>\n<li>The\u00a0<a href=\"https:\/\/arxiv.org\/abs\/1810.04805\" target=\"_blank\" rel=\"noreferrer noopener\">BERT paper<\/a>\u00a0is imminently readable and contains some suggested default hyperparameter settings as a valuable starting point (see Appendix A.3).<\/li>\n<li>Whether or not you\u2019re new to NLP, check out Jay Alammar\u2019s\u00a0<a href=\"http:\/\/jalammar.github.io\/a-visual-guide-to-using-bert-for-the-first-time\/\" target=\"_blank\" rel=\"noreferrer noopener\">\u201cA Visual Guide to Using BERT for the First Time<\/a>\u201d for a charming illustration of BERT\u2019s capabilities.<\/li>\n<li>Also check out\u00a0<a href=\"https:\/\/arxiv.org\/abs\/2004.10703\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>ktrain<\/strong><\/a>, a package that sits atop Keras (which in turn sits atop TensorFlow) that allows you to effortlessly implement BERT in your work.\u00a0<a href=\"https:\/\/medium.com\/u\/4581d07591d5?source=post_page-----487cce9a2020--------------------------------\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">Arun Maiya<\/a>\u00a0developed this powerful library to enable speed to insight for NLP, image recognition, and graph-based approaches.<\/li>\n<\/ul>\t\t\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<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-91b2c84 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"91b2c84\" data-element_type=\"section\" data-e-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=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b6ad021\" data-id=\"b6ad021\" data-element_type=\"column\" data-e-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-cff3aee elementor-widget elementor-widget-heading\" data-id=\"cff3aee\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><span style=\"font-style: normal;font-weight: 700;font-size: 19px;font-family: ProximaNova-Regular\">#4\u2014&nbsp;<\/span><a href=\"https:\/\/arxiv.org\/abs\/1803.03635\" target=\"_blank\" style=\"font-style: normal;font-weight: 400;font-size: 19px\" rel=\"noopener\"><span style=\"font-weight: 700\">The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks<\/span><\/a><span style=\"font-style: normal;font-weight: 700;font-size: 19px;font-family: ProximaNova-Regular\">&nbsp;<\/span><\/h2>\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<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-2f7f28e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2f7f28e\" data-element_type=\"section\" data-e-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=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4004d4d\" data-id=\"4004d4d\" data-element_type=\"column\" data-e-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-9c91dff elementor-widget elementor-widget-text-editor\" data-id=\"9c91dff\" data-element_type=\"widget\" data-e-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\t\t<p><span style=\"font-family: ProximaNova-Regular; font-size: 19px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400;\">While NLP models are getting larger (see GPT-3\u2019s 175 billion parameters), there\u2019s been an orthogonal effort to find smaller, faster, more efficient neural networks. These networks promise quicker runtimes, lower training costs, and less demand for compute resources.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-648fe9e elementor-widget elementor-widget-text-editor\" data-id=\"648fe9e\" data-element_type=\"widget\" data-e-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\t\t<p><span style=\"font-family: ProximaNova-Regular; font-size: 19px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400;\">In this groundbreaking paper, machine learning wiz kids Jonathan Frankle and Michael Carbin outline a pruning approach to uncover sparse sub-networks that can attain comparable performance to the original, significantly larger neural network.<\/span><\/p>\t\t\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<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-bc6c36c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"bc6c36c\" data-element_type=\"section\" data-e-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=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-134dc71\" data-id=\"134dc71\" data-element_type=\"column\" data-e-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-2940946 elementor-widget elementor-widget-image\" data-id=\"2940946\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1024\" height=\"511\" src=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/Screenshot-2023-10-17-at-4.19.18-PM-1024x511.png\" class=\"attachment-large size-large wp-image-33502\" alt=\"\" srcset=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/Screenshot-2023-10-17-at-4.19.18-PM-1024x511.png 1024w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/Screenshot-2023-10-17-at-4.19.18-PM-300x150.png 300w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/Screenshot-2023-10-17-at-4.19.18-PM-768x383.png 768w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/Screenshot-2023-10-17-at-4.19.18-PM-1536x766.png 1536w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/Screenshot-2023-10-17-at-4.19.18-PM-610x304.png 610w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/Screenshot-2023-10-17-at-4.19.18-PM-360x180.png 360w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/Screenshot-2023-10-17-at-4.19.18-PM-750x374.png 750w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/Screenshot-2023-10-17-at-4.19.18-PM-1140x569.png 1140w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/Screenshot-2023-10-17-at-4.19.18-PM.png 1612w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\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<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-243a825 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"243a825\" data-element_type=\"section\" data-e-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=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3ff7f69\" data-id=\"3ff7f69\" data-element_type=\"column\" data-e-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-d4165bb elementor-widget elementor-widget-text-editor\" data-id=\"d4165bb\" data-element_type=\"widget\" data-e-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\t\t<p><span style=\"color: #a0a0a0; font-family: ProximaNova-Regular; font-size: 11px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; text-align: center;\">via\u00a0<\/span><a style=\"font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; font-size: 11px; font-family: ProximaNova-Regular; color: #f70d28; text-align: center;\" href=\"https:\/\/medium.com\/u\/6438fd23c99a?source=post_page-----487cce9a2020--------------------------------\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">Nolan Day<\/a><span style=\"color: #a0a0a0; font-family: ProximaNova-Regular; font-size: 11px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; text-align: center;\">\u2019s \u201c<\/span><a style=\"font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; font-size: 11px; font-family: ProximaNova-Regular; color: #f70d28; text-align: center;\" href=\"https:\/\/towardsdatascience.com\/breaking-down-the-lottery-ticket-hypothesis-ca1c053b3e58\" target=\"_blank\" rel=\"noreferrer noopener\" class=\"broken_link\">Breaking down the Lottery Ticket Hypothesis<\/a><span style=\"color: #a0a0a0; font-family: ProximaNova-Regular; font-size: 11px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; text-align: center;\">\u201d<\/span><\/p>\t\t\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<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-6b91203 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6b91203\" data-element_type=\"section\" data-e-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=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b4d3fc8\" data-id=\"b4d3fc8\" data-element_type=\"column\" data-e-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-e1f4004 elementor-widget elementor-widget-text-editor\" data-id=\"e1f4004\" data-element_type=\"widget\" data-e-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\t\t<p><span style=\"font-family: ProximaNova-Regular; font-size: 19px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400;\">The Lottery Ticket refers to the connections with initial weights that make them particularly effective. The finding offers many advantages in storage, runtime, and computational performance \u2013 and won abest paper award at ICLR 2019. Further research has built on this technique,\u00a0<\/span><a style=\"font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; font-size: 19px; font-family: ProximaNova-Regular;\" href=\"https:\/\/arxiv.org\/abs\/2002.00585\" target=\"_blank\" rel=\"noreferrer noopener\">proving its applicability<\/a><span style=\"font-family: ProximaNova-Regular; font-size: 19px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400;\">\u00a0and\u00a0<\/span><a style=\"font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; font-size: 19px; font-family: ProximaNova-Regular;\" href=\"https:\/\/arxiv.org\/abs\/1911.11134\" target=\"_blank\" rel=\"noreferrer noopener\">applying it to an originally sparse network<\/a><span style=\"font-family: ProximaNova-Regular; font-size: 19px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400;\">.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0d03bc0 elementor-widget elementor-widget-text-editor\" data-id=\"0d03bc0\" data-element_type=\"widget\" data-e-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\t\t<p><span style=\"font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: bold; font-size: 19px; font-family: ProximaNova-Regular;\">How to use<\/span><span style=\"font-family: ProximaNova-Regular; font-size: 19px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400;\">:<br \/><\/span><\/p>\n<ul>\n<li>Consider\u00a0<a href=\"https:\/\/jacobgil.github.io\/deeplearning\/pruning-deep-learning\" target=\"_blank\" rel=\"noreferrer noopener\">pruning<\/a>\u00a0your neural nets before putting them into production. Pruning network weights can reduce the number of parameters by 90%+ while still achieving the same level of performance as the original network.<\/li>\n<li>Also check out this\u00a0<a href=\"https:\/\/thedataexchange.media\/software-and-commodity-hardware-can-handle-deep-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\">episode of the Data Exchange podcast<\/a>, where Ben Lorica talks to\u00a0<a href=\"https:\/\/neuralmagic.com\/about\/\" target=\"_blank\" rel=\"noreferrer noopener\">Neural Magic<\/a>, a startup that\u2019s looking to capitalize on techniques such as\u00a0<a href=\"https:\/\/www.youtube.com\/watch?v=3JWRVx1OKQQ\" target=\"_blank\" rel=\"noreferrer noopener\">pruning and quantization<\/a>\u00a0with a slick UI that makes achieving sparsity easier.<\/li>\n<\/ul>\t\t\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<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-58880d8 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"58880d8\" data-element_type=\"section\" data-e-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=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d4b50a3\" data-id=\"d4b50a3\" data-element_type=\"column\" data-e-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-1d09602 elementor-widget elementor-widget-text-editor\" data-id=\"1d09602\" data-element_type=\"widget\" data-e-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\t\t<p><span style=\"font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: bold; font-size: 19px; font-family: ProximaNova-Regular;\">Read more<\/span><span style=\"font-family: ProximaNova-Regular; font-size: 19px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400;\">:<br \/><\/span><\/p>\n<ul>\n<li><a href=\"https:\/\/ml-retrospectives.github.io\/neurips2019\/accepted_retrospectives\/2019\/lottery-ticket\/\" target=\"_blank\" rel=\"noreferrer noopener\">Check out this interesting sidebar<\/a>\u00a0from one of the \u201cThe Lottery Ticket\u201d authors about flaws in how the machine learning community evaluates good ideas<\/li>\n<\/ul>\n<p id=\"f1e7\"><strong>#5 \u2014<\/strong><a class=\"broken_link\" href=\"https:\/\/www.researchgate.net\/publication\/312395254_Releasing_the_death-grip_of_null_hypothesis_statistical_testing_p_05_Applying_complexity_theory_and_somewhat_precise_outcome_testing_SPOT\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Releasing the death-grip of null hypothesis statistical testing (<em>p<\/em>\u00a0&lt; .05)<\/strong><\/a><strong>\u00a0<\/strong><\/p>\n<blockquote class=\"wp-block-quote\">\n<p>Classical hypothesis testing leads to over-certainty, and produces the false idea that causes have been identified via statistical methods. (<a href=\"http:\/\/wmbriggs.com\/public\/Briggs.ReplacementForHypothesisTesting.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">Read more<\/a>)<\/p>\n<\/blockquote>\n<p id=\"51aa\">Hypothesis testing predates the use of computers. Given the challenges associated with this approach (such as the fact that\u00a0<a href=\"https:\/\/fivethirtyeight.com\/features\/statisticians-found-one-thing-they-can-agree-on-its-time-to-stop-misusing-p-values\/\" target=\"_blank\" rel=\"noreferrer noopener\">even staticians find it nearly impossible to explain p-value<\/a>), it may be time to consider alternatives such as somewhat precise outcome testing (SPOT).<\/p>\t\t\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<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-e5f7a3a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e5f7a3a\" data-element_type=\"section\" data-e-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=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-5f16781\" data-id=\"5f16781\" data-element_type=\"column\" data-e-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-54b4038 elementor-widget elementor-widget-image\" data-id=\"54b4038\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"361\" src=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/Screenshot-2023-10-17-at-4.25.05-PM-1024x361.png\" class=\"attachment-large size-large wp-image-33503\" alt=\"\" srcset=\"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/Screenshot-2023-10-17-at-4.25.05-PM-1024x361.png 1024w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/Screenshot-2023-10-17-at-4.25.05-PM-300x106.png 300w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/Screenshot-2023-10-17-at-4.25.05-PM-768x271.png 768w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/Screenshot-2023-10-17-at-4.25.05-PM-1536x541.png 1536w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/Screenshot-2023-10-17-at-4.25.05-PM-610x215.png 610w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/Screenshot-2023-10-17-at-4.25.05-PM-750x264.png 750w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/Screenshot-2023-10-17-at-4.25.05-PM-1140x402.png 1140w, https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/Screenshot-2023-10-17-at-4.25.05-PM.png 1600w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\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<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-1237007 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1237007\" data-element_type=\"section\" data-e-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=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-8b9c9ec\" data-id=\"8b9c9ec\" data-element_type=\"column\" data-e-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-f6b4d01 elementor-widget elementor-widget-text-editor\" data-id=\"f6b4d01\" data-element_type=\"widget\" data-e-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\t\t<p><span style=\"color: #a0a0a0; font-family: ProximaNova-Regular; font-size: 11px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; text-align: center;\">\u201cSignificant\u201d via\u00a0<\/span><a style=\"font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; font-size: 11px; font-family: ProximaNova-Regular; color: #f70d28; text-align: center;\" href=\"https:\/\/xkcd.com\/882\/\" target=\"_blank\" rel=\"noreferrer noopener\">xkcd<\/a><\/p>\t\t\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<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-eeb7ec1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"eeb7ec1\" data-element_type=\"section\" data-e-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=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-8fc7c7d\" data-id=\"8fc7c7d\" data-element_type=\"column\" data-e-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-f27f6d6 elementor-widget elementor-widget-text-editor\" data-id=\"f27f6d6\" data-element_type=\"widget\" data-e-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\t\t<p id=\"acb6\" style=\"font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; font-family: ProximaNova-Regular;\"><span style=\"font-weight: bold;\">How to use<\/span>:<\/p>\n<ul style=\"padding-left: 2.14286em; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; font-size: 16px; font-family: ProximaNova-Regular;\">\n<li>Check out this blog post, \u201c<a href=\"https:\/\/www.datasciencecentral.com\/profiles\/blogs\/the-death-of-the-statistical-test-of-hypothesis\" target=\"_blank\" rel=\"noreferrer noopener\">The Death of the Statistical Tests of Hypotheses<\/a>,\u201d where a frustrated statistician outlines some of the challenges associated with the classical approach and explains an alternative utilizing confidence intervals<\/li>\n<\/ul>\t\t\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<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-d48640a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d48640a\" data-element_type=\"section\" data-e-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=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d252511\" data-id=\"d252511\" data-element_type=\"column\" data-e-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-9131c96 elementor-widget elementor-widget-heading\" data-id=\"9131c96\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><h2 class=\"wp-block-heading\" id=\"b51d\" style=\"font-style: normal\">Survey Time!<\/h2><\/h2>\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<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-87d6ec0 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"87d6ec0\" data-element_type=\"section\" data-e-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=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-c7a2539\" data-id=\"c7a2539\" data-element_type=\"column\" data-e-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-a68aaac elementor-widget elementor-widget-text-editor\" data-id=\"a68aaac\" data-element_type=\"widget\" data-e-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\t\t<p><span style=\"font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 700; font-size: 19px; font-family: ProximaNova-Regular;\">What articles do you consider to be fundamental to your understanding of data science?<\/span><span style=\"font-family: ProximaNova-Regular; font-size: 19px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400;\">&nbsp;Let me know by dropping a line in the comments.<\/span><br><\/p>\t\t\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>Topics covered in these papers range from the orchestration of the DS workflow to breakthroughs in faster neural networks to a rethinking of our fundamental approach to problem solving with statistics. <\/p>\n","protected":false},"author":940,"featured_media":10632,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[187],"tags":[838,839,92],"ppma_author":[3860],"class_list":["post-10633","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-cloud","tag-ds-workflow","tag-faster-neural-networks","tag-machine-learning"],"authors":[{"term_id":3860,"user_id":940,"is_guest":0,"slug":"nicole-janeway-bills","display_name":"Nicole Janeway","avatar_url":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/10\/Nicole-Janeway-Bills-150x150.jpg","user_url":"https:\/\/page.co\/ahje9p","last_name":"Janeway","first_name":"Nicole","job_title":"","description":"Nicole Janeway, a Data Scientist at Atlas Research, has  experience in commercial and federal consulting.  She helps organizations leverage their top asset:  a simple and robust Data Strategy. <a href=\"https:\/\/page.co\/ahje9p\/\"> Sign up<\/a> for more of her writing."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/10633","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/940"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=10633"}],"version-history":[{"count":7,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/10633\/revisions"}],"predecessor-version":[{"id":33506,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/10633\/revisions\/33506"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/10632"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=10633"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=10633"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=10633"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=10633"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}