fix documentation links

docs-src/docs/regularization.md

@@ -45,12 +45,12 @@ In [Dense-Sparse-Dense (DSD)](#han-et-al-2017), Song Han et al. use pruning as a
 
 Regularization can also be used to induce sparsity.  To induce element-wise sparsity we can use the \\(l_1\\)-norm, \\(\lVert W \rVert_1\\).
 \\[
-l_1(W) = \sum_{i=1}^{|W|} |w_i|
+\lVert W \rVert_1 = l_1(W) = \sum_{i=1}^{|W|} |w_i|
 \\]
 
 \\(l_2\\)-norm regularization reduces overfitting and improves a model's accuracy by shrinking large parameters, but it does not force these parameters to absolute zero.  \\(l_1\\)-norm regularization sets some of the parameter elements to zero, therefore limiting the model's capacity while making the model simpler.  This is sometimes referred to as *feature selection* and gives us another interpretation of pruning.
 
-[One](http://localhost:8888/notebooks/L1-regularization.ipynb) of Distiller's Jupiter notebooks explains how the \\(l_1\\)-norm regularizer induces sparsity, and how it interacts with \\(l_2\\)-norm regularization.
+[One](https://github.com/NervanaSystems/distiller/blob/master/jupyter/L1-regularization.ipynb) of Distiller's Jupiter notebooks explains how the \\(l_1\\)-norm regularizer induces sparsity, and how it interacts with \\(l_2\\)-norm regularization.
 
 
 If we configure ```weight_decay``` to zero and use \\(l_1\\)-norm regularization, then we have:
@@ -71,7 +71,7 @@ loss = criterion(output, target) + lambda * l1_regularizer()
 
 ## Group Regularization
 
-In Group Regularization, we penalize entire groups of parameter elements, instead of individual elements.  Therefore, entire groups are either sparsified (i.e. all of the group elements have a value of zero) or not.  The groups structures have to be pre-defined.
+In Group Regularization, we penalize entire groups of parameter elements, instead of individual elements.  Therefore, entire groups are either sparsified (i.e. all of the group elements have a value of zero) or not.  The group structures have to be pre-defined.
 
 To the data loss, and the element-wise regularization (if any), we can add group-wise regularization penalty.  We represent all of the parameter groups in layer \\(l\\) as \\( W_l^{(G)} \\), and we add the penalty of all groups for all layers.  It gets a bit messy, but not overly complicated:
 \\[
@@ -91,7 +91,7 @@ where \\(w^{(g)} \in w^{(l)} \\) and \\( |w^{(g)}| \\) is the number of elements
 Group Regularization is also called Block Regularization, Structured Regularization, or coarse-grained sparsity (remember that element-wise sparsity is sometimes referred to as fine-grained sparsity).  Group sparsity exhibits regularity (i.e. its shape is regular), and therefore
 it can be beneficial to improve inference speed.
 
-[Huizi-et-al-2017](#huizi-et-al-2017) provides an overview of some of the different groups: kernel, channel, filter, layers.  Fiber structures such as matrix columns and rows, as well as various shaped structures (block sparsity), and even [intra kernel strided sparsity](#anwar-et-al-2015).
+[Huizi-et-al-2017](#huizi-et-al-2017) provides an overview of some of the different groups: kernel, channel, filter, layers.  Fiber structures such as matrix columns and rows, as well as various shaped structures (block sparsity), and even [intra kernel strided sparsity](#anwar-et-al-2015) can also be used.
 
 ```distiller.GroupLassoRegularizer``` currently implements most of these groups, and you can easily add new groups.
 

docs/index.html

@@ -236,5 +236,5 @@ And of course, if we used a sparse or compressed representation, then we are red
 
 <!--
 MkDocs version : 0.17.2
-Build Date UTC : 2018-04-24 15:37:02
+Build Date UTC : 2018-04-24 17:18:11
 -->

docs/regularization/index.html

@@ -200,10 +200,10 @@ for input, target in dataset:
 </blockquote>
 <p>Regularization can also be used to induce sparsity.  To induce element-wise sparsity we can use the \(l_1\)-norm, \(\lVert W \rVert_1\).
 \[
-l_1(W) = \sum_{i=1}^{|W|} |w_i|
+\lVert W \rVert_1 = l_1(W) = \sum_{i=1}^{|W|} |w_i|
 \]</p>
 <p>\(l_2\)-norm regularization reduces overfitting and improves a model's accuracy by shrinking large parameters, but it does not force these parameters to absolute zero.  \(l_1\)-norm regularization sets some of the parameter elements to zero, therefore limiting the model's capacity while making the model simpler.  This is sometimes referred to as <em>feature selection</em> and gives us another interpretation of pruning.</p>
-<p><a href="http://localhost:8888/notebooks/L1-regularization.ipynb">One</a> of Distiller's Jupiter notebooks explains how the \(l_1\)-norm regularizer induces sparsity, and how it interacts with \(l_2\)-norm regularization.</p>
+<p><a href="https://github.com/NervanaSystems/distiller/blob/master/jupyter/L1-regularization.ipynb">One</a> of Distiller's Jupiter notebooks explains how the \(l_1\)-norm regularizer induces sparsity, and how it interacts with \(l_2\)-norm regularization.</p>
 <p>If we configure <code>weight_decay</code> to zero and use \(l_1\)-norm regularization, then we have:
 \[
 loss(W;x;y) = loss_D(W;x;y) + \lambda_R \lVert W \rVert_1
@@ -219,7 +219,7 @@ loss = criterion(output, target) + lambda * l1_regularizer()
 </code></pre>
 
 <h2 id="group-regularization">Group Regularization</h2>
-<p>In Group Regularization, we penalize entire groups of parameter elements, instead of individual elements.  Therefore, entire groups are either sparsified (i.e. all of the group elements have a value of zero) or not.  The groups structures have to be pre-defined.</p>
+<p>In Group Regularization, we penalize entire groups of parameter elements, instead of individual elements.  Therefore, entire groups are either sparsified (i.e. all of the group elements have a value of zero) or not.  The group structures have to be pre-defined.</p>
 <p>To the data loss, and the element-wise regularization (if any), we can add group-wise regularization penalty.  We represent all of the parameter groups in layer \(l\) as \( W_l^{(G)} \), and we add the penalty of all groups for all layers.  It gets a bit messy, but not overly complicated:
 \[
 loss(W;x;y) = loss_D(W;x;y) + \lambda_R R(W) + \lambda_g \sum_{l=1}^{L} R_g(W_l^{(G)})
@@ -233,7 +233,7 @@ where \(w^{(g)} \in w^{(l)} \) and \( |w^{(g)}| \) is the number of elements in
 <br>
 Group Regularization is also called Block Regularization, Structured Regularization, or coarse-grained sparsity (remember that element-wise sparsity is sometimes referred to as fine-grained sparsity).  Group sparsity exhibits regularity (i.e. its shape is regular), and therefore
 it can be beneficial to improve inference speed.</p>
-<p><a href="#huizi-et-al-2017">Huizi-et-al-2017</a> provides an overview of some of the different groups: kernel, channel, filter, layers.  Fiber structures such as matrix columns and rows, as well as various shaped structures (block sparsity), and even <a href="#anwar-et-al-2015">intra kernel strided sparsity</a>.</p>
+<p><a href="#huizi-et-al-2017">Huizi-et-al-2017</a> provides an overview of some of the different groups: kernel, channel, filter, layers.  Fiber structures such as matrix columns and rows, as well as various shaped structures (block sparsity), and even <a href="#anwar-et-al-2015">intra kernel strided sparsity</a> can also be used.</p>
 <p><code>distiller.GroupLassoRegularizer</code> currently implements most of these groups, and you can easily add new groups.</p>
 <h2 id="references">References</h2>
 <p><div id="deep-learning"></div> <strong>Ian Goodfellow and Yoshua Bengio and Aaron Courville</strong>.