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rstudio · Jan 7, 2019 · 4f1d358 · 4f1d358
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diff --git a/docs/index.html b/docs/index.html
@@ -1339,7 +1339,7 @@
   padding-bottom: 12px;
 }
 .posts-list {
-  margin-top: 24px;
+  margin-top: 60px;
   margin-bottom: 24px;
 }
 
@@ -1474,9 +1474,6 @@
   .posts-list .post-preview h2 {
     font-size: 26px;
   }
-  .posts-list {
-    margin-top: 60px;
-  }
   .posts-list .post-preview .thumbnail {
     float: right;
     width: 35%;
@@ -1828,7 +1825,7 @@ <h2>Classifying physical activity from smartphone data</h2>
 <a href="posts/2018-06-25-sunspots-lstm/" class="post-preview">
 <script class="post-metadata" type="text/json">{"categories":["Keras","Examples","Time Series"]}</script>
 <div class="metadata">
-<div class="publishedDate">June 24, 2018</div>
+<div class="publishedDate">June 25, 2018</div>
 </div>
 <div class="thumbnail">
 <img data-src="posts/2018-06-25-sunspots-lstm/images/backtested_test.png"/>

diff --git a/docs/index.xml b/docs/index.xml
@@ -156,7 +156,7 @@ In this post, we explain the steps involved in coding a basic single-shot object
       <link>https://blogs.rstudio.com/tensorflow/posts/2018-06-25-sunspots-lstm</link>
       <description>In this post we will examine making time series predictions using the sunspots dataset that ships with base R. Sunspots are dark spots on the sun, associated with lower temperature. Our post will focus on both how to apply deep learning to time series forecasting, and how to properly apply cross validation in this domain.</description>
       <guid>https://blogs.rstudio.com/tensorflow/posts/2018-06-25-sunspots-lstm</guid>
-      <pubDate>Sun, 24 Jun 2018 00:00:00 +0000</pubDate>
+      <pubDate>Mon, 25 Jun 2018 00:00:00 +0000</pubDate>
       <media:content url="https://blogs.rstudio.com/tensorflow/posts/2018-06-25-sunspots-lstm/images/backtested_test.png" medium="image" type="image/png" width="800" height="416"/>
     </item>
     <item>

diff --git a/docs/posts/2018-06-25-sunspots-lstm/index.html b/docs/posts/2018-06-25-sunspots-lstm/index.html
@@ -407,9 +407,8 @@
 
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+<link href="../../site_libs/font-awesome-5.1.0/css/all.css" rel="stylesheet"/>
+<link href="../../site_libs/font-awesome-5.1.0/css/v4-shims.css" rel="stylesheet"/>
 <script src="../../site_libs/headroom-0.9.4/headroom.min.js"></script>
 <!--/radix_placeholder_navigation_in_header-->
   <!--radix_placeholder_distill-->
@@ -555,6 +554,10 @@
 /* Tweak code blocks (note that this CSS is repeated above in an injection
    into the d-code shadow dom) */
 
+d-code {
+  overflow-x: auto !important;
+}
+
 pre.d-code code.d-code {
   padding-left: 10px;
   font-size: 12px;
@@ -585,6 +588,11 @@
 }
 
 @media(min-width: 768px) {
+
+d-code {
+  overflow-x: visible !important;
+}
+
 pre.d-code code.d-code  {
     padding-left: 18px;
     font-size: 14px;
@@ -1299,7 +1307,7 @@
 <!--radix_placeholder_front_matter-->
 
 <script id="distill-front-matter" type="text/json">
-{"title":"Predicting Sunspot Frequency with Keras","description":"In this post we will examine making time series predictions using the sunspots dataset that ships with base R. Sunspots are dark spots on the sun, associated with lower temperature. Our post will focus on both how to apply deep learning to time series forecasting, and how to properly apply cross validation in this domain.","authors":[{"author":"Matt Dancho","authorURL":"https://github.com/mdancho84","affiliation":"Business Science","affiliationURL":"https://www.business-science.io/"},{"author":"Sigrid Keydana","authorURL":"https://github.com/skeydan","affiliation":"RStudio","affiliationURL":"https://www.rstudio.com"}],"publishedDate":"2018-06-25T00:00:00.000+02:00","citationText":"Dancho & Keydana, 2018"}
+{"title":"Predicting Sunspot Frequency with Keras","description":"In this post we will examine making time series predictions using the sunspots dataset that ships with base R. Sunspots are dark spots on the sun, associated with lower temperature. Our post will focus on both how to apply deep learning to time series forecasting, and how to properly apply cross validation in this domain.","authors":[{"author":"Matt Dancho","authorURL":"https://github.com/mdancho84","affiliation":"Business Science","affiliationURL":"https://www.business-science.io/"},{"author":"Sigrid Keydana","authorURL":"https://github.com/skeydan","affiliation":"RStudio","affiliationURL":"https://www.rstudio.com"}],"publishedDate":"2018-06-25T00:00:00.000-04:00","citationText":"Dancho & Keydana, 2018"}
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diff --git a/docs/posts/posts.json b/docs/posts/posts.json
@@ -323,7 +323,7 @@
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diff --git a/docs/sitemap.xml b/docs/sitemap.xml
@@ -2,19 +2,19 @@
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     <loc>https://blogs.rstudio.com/tensorflow/about.html</loc>
-    <lastmod>2018-11-15T08:25:53+01:00</lastmod>
+    <lastmod>2018-09-12T12:45:47-04:00</lastmod>
   </url>
   <url>
     <loc>https://blogs.rstudio.com/tensorflow/contributing.html</loc>
-    <lastmod>2018-11-15T08:25:53+01:00</lastmod>
+    <lastmod>2018-09-12T12:45:47-04:00</lastmod>
   </url>
   <url>
     <loc>https://blogs.rstudio.com/tensorflow/gallery.html</loc>
-    <lastmod>2018-11-15T08:25:54+01:00</lastmod>
+    <lastmod>2018-11-20T06:05:15-05:00</lastmod>
   </url>
   <url>
     <loc>https://blogs.rstudio.com/tensorflow/</loc>
-    <lastmod>2018-11-15T08:25:54+01:00</lastmod>
+    <lastmod>2018-10-09T09:43:20-04:00</lastmod>
   </url>
   <url>
     <loc>https://blogs.rstudio.com/tensorflow/posts/2018-12-18-object-detection-concepts/</loc>
@@ -78,7 +78,7 @@
   </url>
   <url>
     <loc>https://blogs.rstudio.com/tensorflow/posts/2018-06-25-sunspots-lstm/</loc>
-    <lastmod>2019-01-04T15:54:11+01:00</lastmod>
+    <lastmod>2019-01-07T09:09:45-05:00</lastmod>
   </url>
   <url>
     <loc>https://blogs.rstudio.com/tensorflow/posts/2018-06-06-simple-audio-classification-keras/</loc>