{"id":569,"date":"2018-06-01T06:34:34","date_gmt":"2018-06-01T06:34:34","guid":{"rendered":"https:\/\/www.aiproblog.com\/index.php\/2018\/06\/01\/machine-learning-with-c-polynomial-regression-on-gpu\/"},"modified":"2018-06-01T06:34:34","modified_gmt":"2018-06-01T06:34:34","slug":"machine-learning-with-c-polynomial-regression-on-gpu","status":"publish","type":"post","link":"https:\/\/www.aiproblog.com\/index.php\/2018\/06\/01\/machine-learning-with-c-polynomial-regression-on-gpu\/","title":{"rendered":"Machine Learning with C++ – Polynomial Regression on GPU"},"content":{"rendered":"

Author: Kyrylo Kolodiazhnyi<\/p>\n

\n

Hello, this is my second article about how to use modern C++ for solving machine learning problems. This time I will show how to make a model for polynomial regression problem described in previous article<\/a>, but now with another library which allows you to use your GPU easily.<\/p>\n

<\/a><\/p>\n

For this tutorial I chose MShadow<\/a> library, you can find documentation for it here<\/a>. This library was chosen because it is actively developed now, and used as a basis for one of a wide used deep learning framework MXNet<\/a>. Also it is a header only library with minimal dependencies, so it’s integration is not hard at all.<\/p>\n

Continue reading the article and source code here<\/a>. Please feel free to leave comment or create issue in repository if you find some mistakes.<\/p>\n

Content<\/strong><\/p>\n

    \n
  1. Preparations<\/li>\n
  2. Loading data to MShadow data-structures<\/li>\n
  3. Standardization<\/li>\n
  4. Generating additional polynomial components<\/li>\n
  5. Generating new data for testing model predictions<\/li>\n
  6. Batch gradient descent implementation<\/li>\n
  7. Training the regression model<\/li>\n
  8. Making predictions<\/li>\n
  9. Plot results<\/li>\n<\/ol>\n<\/div>\n

    Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

    Author: Kyrylo Kolodiazhnyi Hello, this is my second article about how to use modern C++ for solving machine learning problems. This time I will show […] Read More<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":570,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[26],"tags":[],"_links":{"self":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/569"}],"collection":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/comments?post=569"}],"version-history":[{"count":0,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/569\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media\/570"}],"wp:attachment":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media?parent=569"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/categories?post=569"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/tags?post=569"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}