{"id":7544,"date":"2023-03-03T21:00:23","date_gmt":"2023-03-03T21:00:23","guid":{"rendered":"https:\/\/www.goodacademic.com\/blog\/questions\/gradients-for-learning\/"},"modified":"2023-03-03T21:00:23","modified_gmt":"2023-03-03T21:00:23","slug":"gradients-for-learning","status":"publish","type":"questions","link":"https:\/\/www.goodacademic.com\/blog\/questions\/gradients-for-learning\/","title":{"rendered":"Gradients for learning"},"content":{"rendered":"<div class=\"col-sm-12 messageContent\">\n <b>Learning Goal: <\/b>I&#8217;m working on a data engineering discussion question and need the explanation and answer to help me learn.<\/p>\n<p>In section 10.7 The Challenge of Long-Term Dependencies, from &#8220;Goodfellow, I., Bengio, Y., Courville, A. (2016). <em>Deep Learning.<\/em> United Kingdom: MIT Press,&#8221; authors discuss the mathematical challenge of learning long-term dependencies in recurrent networks. The basic problem is that gradients propagated over many stages tend to either vanish or explode. Discuss the following:<\/p>\n<ol>\n<li>why the gradients propagated over many stages tend to either vanish or explode<\/li>\n<li>possible ways to deal with the gradients vanishing or exploding<\/li>\n<\/ol>\n<p>To participate in the discussion, respond to the discussion promptly by Thursday at 11:59PM EST. Then, read a selection of your colleagues&#8217; postings. Finally, respond to at least two classmates by Sunday at 11:59PM EST in one or more of the following ways:<\/p>\n<ul>\n<li>Complete the discussion by the assigned due date.<\/li>\n<li>Do not claim credit for the words, ideas, and concepts of others.<\/li>\n<li>Do not copy and paste information or concepts from the Internet and claim that it is your work. It will be considered Plagiarism and you will receive a zero for your work.<\/li>\n<\/ul>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Learning Goal: I&#8217;m working on a data engineering discussion question and need the explanation and answer to help me learn. In section 10.7 The Challenge of Long-Term Dependencies, from &#8220;Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep Learning. United Kingdom: MIT Press,&#8221; authors discuss the mathematical challenge of learning long-term dependencies in recurrent networks. The [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","meta":[],"disciplines":[848],"paper_types":[],"tagged":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/questions\/7544"}],"collection":[{"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/questions"}],"about":[{"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/types\/questions"}],"author":[{"embeddable":true,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/comments?post=7544"}],"version-history":[{"count":0,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/questions\/7544\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/media?parent=7544"}],"wp:term":[{"taxonomy":"disciplines","embeddable":true,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/disciplines?post=7544"},{"taxonomy":"paper_types","embeddable":true,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/paper_types?post=7544"},{"taxonomy":"tagged","embeddable":true,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/tagged?post=7544"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}