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diff --git a/docs/posts/2019-12-16-TensorFlow-Polynomial-Regression.html b/docs/posts/2019-12-16-TensorFlow-Polynomial-Regression.html index 51c31d8..a34dcbf 100644 --- a/docs/posts/2019-12-16-TensorFlow-Polynomial-Regression.html +++ b/docs/posts/2019-12-16-TensorFlow-Polynomial-Regression.html @@ -8,9 +8,9 @@ <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Polynomial Regression Using TensorFlow</title> <meta name="og:site_name" content="Navan Chauhan" /> - <link rel="canonical" href="https://web.navan.dev/" /> - <meta name="twitter:url" content="https://web.navan.dev/" /> - <meta name="og:url" content="https://web.navan.dev/" /> + <link rel="canonical" href="https://web.navan.dev/posts/2019-12-16-TensorFlow-Polynomial-Regression.html" /> + <meta name="twitter:url" content="https://web.navan.dev/posts/2019-12-16-TensorFlow-Polynomial-Regression.html /> + <meta name="og:url" content="https://web.navan.dev/posts/2019-12-16-TensorFlow-Polynomial-Regression.html" /> <meta name="twitter:title" content="Polynomial Regression Using TensorFlow" /> <meta name="og:title" content="Polynomial Regression Using TensorFlow" /> <meta name="description" content="Polynomial regression using TensorFlow" /> @@ -22,7 +22,6 @@ <link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan" /> <meta name="twitter:image" content="https://web.navan.dev/images/opengraph/posts/2019-12-16-TensorFlow-Polynomial-Regression.png" /> <meta name="og:image" content="https://web.navan.dev/images/opengraph/posts/2019-12-16-TensorFlow-Polynomial-Regression.png" /> - <link rel="manifest" href="manifest.json" /> <meta name="google-site-verification" content="LVeSZxz-QskhbEjHxOi7-BM5dDxTg53x2TwrjFxfL0k" /> <script data-goatcounter="https://navanchauhan.goatcounter.com/count" async src="//gc.zgo.at/count.js"></script> @@ -586,15 +585,13 @@ values using the X values. We then plot it to compare the actual data and predic <p>Basically if you train your machine learning model on a small dataset for a really large number of epochs, the model will learn all the deformities/noise in the data and will actually think that it is a normal part. Therefore when it will see some new data, it will discard that new data as noise and will impact the accuracy of the model in a negative manner</p> <blockquote>If you have scrolled this far, consider subscribing to my mailing list <a href="https://listmonk.navan.dev/subscription/form">here.</a> You can subscribe to either a specific type of post you are interested in, or subscribe to everything with the "Everything" list.</blockquote> - <script data-isso="//comments.navan.dev/" - src="//comments.navan.dev/js/embed.min.js"></script> + <script data-isso="https://comments.navan.dev/" + src="https://comments.navan.dev/js/embed.min.js"></script> <section id="isso-thread"> <noscript>Javascript needs to be activated to view comments.</noscript> </section> </main> -<script src="assets/manup.min.js"></script> -<script src="/pwabuilder-sw-register.js"></script> </body> </html>
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