Updated github links

posts/2014-07-Conv-Nets-Modular/index.html

@@ -288,7 +288,7 @@ <h2 id="formalizing-convolutional-neural-networks">Formalizing Convolutional Neu
 <h2 id="next-posts-in-this-series">Next Posts in this Series</h2>
 <p><a href="../2014-07-Understanding-Convolutions/"><strong>Read the next post!</strong></a></p>
 <p>This post is part of a series on convolutional neural networks and their generalizations. The first two posts will be review for those familiar with deep learning, while later ones should be of interest to everyone. To get updates, subscribe to my <a href="../../rss.xml">RSS feed</a>!</p>
-<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/Conv-Nets-Series">github</a>.</p>
+<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/colah.github.io">github</a>.</p>
 <h2 id="acknowledgments">Acknowledgments</h2>
 <p>I’m grateful to Eliana Lorch, Aaron Courville, and Sebastian Zany for their comments and support.</p>
 <section class="footnotes">

posts/2014-07-Understanding-Convolutions/index.html

@@ -313,7 +313,7 @@ <h2 id="conclusion">Conclusion</h2>
 <p>In fact, the use of highly-efficient parallel convolution implementations on GPUs has been essential to recent progress in computer vision.</p>
 <h2 id="next-posts-in-this-series">Next Posts in this Series</h2>
 <p>This post is part of a series on convolutional neural networks and their generalizations. The first two posts will be review for those familiar with deep learning, while later ones should be of interest to everyone. To get updates, subscribe to my <a href="../../rss.xml">RSS feed</a>!</p>
-<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/Conv-Nets-Series">github</a>.</p>
+<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/colah.github.io">github</a>.</p>
 <h2 id="acknowledgments">Acknowledgments</h2>
 <p>I’m extremely grateful to Eliana Lorch, for extensive discussion of convolutions and help writing this post.</p>
 <p>I’m also grateful to Michael Nielsen and Dario Amodei for their comments and support.</p>

posts/2014-12-Groups-Convolution/index.html

@@ -297,7 +297,7 @@ <h2 id="conclusion">Conclusion</h2>
 <p>Group convolutions provide elegant language for talking about lots of situations involving probability. But, since this is a series of blog posts on <em>convolutional neural networks</em>, you may suspect that I have other interests in them. Well, you guessed correctly. Group convolutions naturally extend convolutional neural networks, with everything fitting together extremely nicely. Since convolutional neural networks are one of the most powerful tools in machine learning right now, that’s pretty interesting. In our next post, we will explore these networks.</p>
 <h2 id="next-posts-in-this-series">Next Posts in this Series</h2>
 <p>This post is part of a series on convolutional neural networks and their generalizations. The first two posts will be review for those familiar with deep learning, while later ones should be of interest to everyone. To get updates, subscribe to my <a href="../../rss.xml">RSS feed</a>!</p>
-<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/group-conv-post">github</a>.</p>
+<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/colah.github.io">github</a>.</p>
 <h2 id="acknowledgements">Acknowledgements</h2>
 <p>I’m grateful to Yomna Nasser, Harry de Valence, Sam Eisenstat, and Sebastian Zany for taking the time to read and comment on draft version of this post – their feedback improved it a lot!</p>
 <p>I’m also grateful to Guillaume Alain, Eliana Lorch, Dario Amodei, Aaron Courville, Yoshua Bengio, and Michael Nielsen for discussion of group convolution and its potential applications to neural networks.</p>

posts/tags/convolution.xml

@@ -229,7 +229,7 @@ w_0 & w_1 &  0  &  0  & ...\\
 <p>In fact, the use of highly-efficient parallel convolution implementations on GPUs has been essential to recent progress in computer vision.</p>
 <h1 id="next-posts-in-this-series">Next Posts in this Series</h1>
 <p>This post is part of a series on convolutional neural networks and their generalizations. The first two posts will be review for those familiar with deep learning, while later ones should be of interest to everyone. To get updates, subscribe to my <a href="../../rss.xml">RSS feed</a>!</p>
-<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/Conv-Nets-Series">github</a>.</p>
+<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/colah.github.io">github</a>.</p>
 <h1 id="acknowledgments">Acknowledgments</h1>
 <p>I’m extremely grateful to Eliana Lorch, for extensive discussion of convolutions and help writing this post.</p>
 <p>I’m also grateful to Michael Nielsen and Dario Amodei for their comments and support.</p>

posts/tags/convolutional neural networks.xml

@@ -229,7 +229,7 @@ w_0 & w_1 &  0  &  0  & ...\\
 <p>In fact, the use of highly-efficient parallel convolution implementations on GPUs has been essential to recent progress in computer vision.</p>
 <h1 id="next-posts-in-this-series">Next Posts in this Series</h1>
 <p>This post is part of a series on convolutional neural networks and their generalizations. The first two posts will be review for those familiar with deep learning, while later ones should be of interest to everyone. To get updates, subscribe to my <a href="../../rss.xml">RSS feed</a>!</p>
-<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/Conv-Nets-Series">github</a>.</p>
+<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/colah.github.io">github</a>.</p>
 <h1 id="acknowledgments">Acknowledgments</h1>
 <p>I’m extremely grateful to Eliana Lorch, for extensive discussion of convolutions and help writing this post.</p>
 <p>I’m also grateful to Michael Nielsen and Dario Amodei for their comments and support.</p>
@@ -444,7 +444,7 @@ Filters learned by the first convolutional layer. The top half corresponds to th
 <h1 id="next-posts-in-this-series">Next Posts in this Series</h1>
 <p><a href="../2014-07-Understanding-Convolutions/"><strong>Read the next post!</strong></a></p>
 <p>This post is part of a series on convolutional neural networks and their generalizations. The first two posts will be review for those familiar with deep learning, while later ones should be of interest to everyone. To get updates, subscribe to my <a href="../../rss.xml">RSS feed</a>!</p>
-<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/Conv-Nets-Series">github</a>.</p>
+<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/colah.github.io">github</a>.</p>
 <h1 id="acknowledgments">Acknowledgments</h1>
 <p>I’m grateful to Eliana Lorch, Aaron Courville, and Sebastian Zany for their comments and support.</p>
 <section class="footnotes">

posts/tags/deep learning.xml

@@ -968,7 +968,7 @@ Filters learned by the first convolutional layer. The top half corresponds to th
 <h1 id="next-posts-in-this-series">Next Posts in this Series</h1>
 <p><a href="../2014-07-Understanding-Convolutions/"><strong>Read the next post!</strong></a></p>
 <p>This post is part of a series on convolutional neural networks and their generalizations. The first two posts will be review for those familiar with deep learning, while later ones should be of interest to everyone. To get updates, subscribe to my <a href="../../rss.xml">RSS feed</a>!</p>
-<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/Conv-Nets-Series">github</a>.</p>
+<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/colah.github.io">github</a>.</p>
 <h1 id="acknowledgments">Acknowledgments</h1>
 <p>I’m grateful to Eliana Lorch, Aaron Courville, and Sebastian Zany for their comments and support.</p>
 <section class="footnotes">

posts/tags/math.xml

@@ -229,7 +229,7 @@ w_0 & w_1 &  0  &  0  & ...\\
 <p>In fact, the use of highly-efficient parallel convolution implementations on GPUs has been essential to recent progress in computer vision.</p>
 <h1 id="next-posts-in-this-series">Next Posts in this Series</h1>
 <p>This post is part of a series on convolutional neural networks and their generalizations. The first two posts will be review for those familiar with deep learning, while later ones should be of interest to everyone. To get updates, subscribe to my <a href="../../rss.xml">RSS feed</a>!</p>
-<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/Conv-Nets-Series">github</a>.</p>
+<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/colah.github.io">github</a>.</p>
 <h1 id="acknowledgments">Acknowledgments</h1>
 <p>I’m extremely grateful to Eliana Lorch, for extensive discussion of convolutions and help writing this post.</p>
 <p>I’m also grateful to Michael Nielsen and Dario Amodei for their comments and support.</p>

posts/tags/modular neural networks.xml

@@ -203,7 +203,7 @@ Filters learned by the first convolutional layer. The top half corresponds to th
 <h1 id="next-posts-in-this-series">Next Posts in this Series</h1>
 <p><a href="../2014-07-Understanding-Convolutions/"><strong>Read the next post!</strong></a></p>
 <p>This post is part of a series on convolutional neural networks and their generalizations. The first two posts will be review for those familiar with deep learning, while later ones should be of interest to everyone. To get updates, subscribe to my <a href="../../rss.xml">RSS feed</a>!</p>
-<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/Conv-Nets-Series">github</a>.</p>
+<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/colah.github.io">github</a>.</p>
 <h1 id="acknowledgments">Acknowledgments</h1>
 <p>I’m grateful to Eliana Lorch, Aaron Courville, and Sebastian Zany for their comments and support.</p>
 <section class="footnotes">

posts/tags/neural networks.xml

@@ -994,7 +994,7 @@ w_0 & w_1 &  0  &  0  & ...\\
 <p>In fact, the use of highly-efficient parallel convolution implementations on GPUs has been essential to recent progress in computer vision.</p>
 <h1 id="next-posts-in-this-series">Next Posts in this Series</h1>
 <p>This post is part of a series on convolutional neural networks and their generalizations. The first two posts will be review for those familiar with deep learning, while later ones should be of interest to everyone. To get updates, subscribe to my <a href="../../rss.xml">RSS feed</a>!</p>
-<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/Conv-Nets-Series">github</a>.</p>
+<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/colah.github.io">github</a>.</p>
 <h1 id="acknowledgments">Acknowledgments</h1>
 <p>I’m extremely grateful to Eliana Lorch, for extensive discussion of convolutions and help writing this post.</p>
 <p>I’m also grateful to Michael Nielsen and Dario Amodei for their comments and support.</p>
@@ -1209,7 +1209,7 @@ Filters learned by the first convolutional layer. The top half corresponds to th
 <h1 id="next-posts-in-this-series">Next Posts in this Series</h1>
 <p><a href="../2014-07-Understanding-Convolutions/"><strong>Read the next post!</strong></a></p>
 <p>This post is part of a series on convolutional neural networks and their generalizations. The first two posts will be review for those familiar with deep learning, while later ones should be of interest to everyone. To get updates, subscribe to my <a href="../../rss.xml">RSS feed</a>!</p>
-<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/Conv-Nets-Series">github</a>.</p>
+<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/colah.github.io">github</a>.</p>
 <h1 id="acknowledgments">Acknowledgments</h1>
 <p>I’m grateful to Eliana Lorch, Aaron Courville, and Sebastian Zany for their comments and support.</p>
 <section class="footnotes">

posts/tags/probability.xml

@@ -229,7 +229,7 @@ w_0 & w_1 &  0  &  0  & ...\\
 <p>In fact, the use of highly-efficient parallel convolution implementations on GPUs has been essential to recent progress in computer vision.</p>
 <h1 id="next-posts-in-this-series">Next Posts in this Series</h1>
 <p>This post is part of a series on convolutional neural networks and their generalizations. The first two posts will be review for those familiar with deep learning, while later ones should be of interest to everyone. To get updates, subscribe to my <a href="../../rss.xml">RSS feed</a>!</p>
-<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/Conv-Nets-Series">github</a>.</p>
+<p>Please comment below or on the side. Pull requests can be made on <a href="https://github.com/colah/colah.github.io">github</a>.</p>
 <h1 id="acknowledgments">Acknowledgments</h1>
 <p>I’m extremely grateful to Eliana Lorch, for extensive discussion of convolutions and help writing this post.</p>
 <p>I’m also grateful to Michael Nielsen and Dario Amodei for their comments and support.</p>