fix typo: Jupyter spelled as Jupiter

README.md

@@ -39,7 +39,7 @@ Network compression can reduce the memory footprint of a neural network, increas
   + [Install dependencies](#install-dependencies)
 * [Getting Started](#getting-started)
   + [Example invocations of the sample application](#example-invocations-of-the-sample-application)
-  + [Explore the sample Jupiter notebooks](#explore-the-sample-jupiter-notebooks)
+  + [Explore the sample Jupyter notebooks](#explore-the-sample-jupyter-notebooks)
 * [Set up the classification datasets](#set-up-the-classification-datasets)
 * [Running the tests](#running-the-tests)
 * [Generating the HTML documentation site](#generating-the-html-documentation-site)
@@ -137,7 +137,7 @@ You can jump head-first into some limited examples of network compression, to ge
 
 Distiller comes with a sample application for compressing image classification DNNs, ```compress_classifier.py``` located at ```distiller/examples/classifier_compression```.
 
-We'll show you how to use it for some simple use-cases, and will point you to some ready-to-go Jupiter notebooks.
+We'll show you how to use it for some simple use-cases, and will point you to some ready-to-go Jupyter notebooks.
 
 For more details, there are some other resources you can refer to:
 + [Model zoo](https://nervanasystems.github.io/distiller/model_zoo/index.html)
@@ -196,7 +196,7 @@ This example performs 8-bit quantization of ResNet20 for CIFAR10.  We've include
 $ python3 compress_classifier.py -a resnet20-cifar ../../../data.cifar10 --resume ../examples/ssl/checkpoints/checkpoint_trained_dense.pth.tar --quantize --evaluate
 ```
 
-### Explore the sample Jupiter notebooks
+### Explore the sample Jupyter notebooks
 The set of notebooks that come with Distiller is described [here](https://nervanasystems.github.io/distiller/jupyter/index.html#using-the-distiller-notebooks), which also explains the steps to install the Jupyter notebook server.<br>
 After installing and running the server, take a look at the [notebook](https://github.com/NervanaSystems/distiller/blob/master/jupyter/sensitivity_analysis.ipynb) covering pruning sensitivity analysis.
 

docs-src/docs/install.md

@@ -5,7 +5,7 @@ These instructions will help get Distiller up and running on your local machine.
 You may also want to refer to these resources:
 
 * [Dataset installation](https://github.com/NervanaSystems/distiller#set-up-the-classification-datasets) instructions.
-* [Jupiter installation](https://nervanasystems.github.io/distiller/jupyter/index.html#installation) instructions.
+* [Jupyter installation](https://nervanasystems.github.io/distiller/jupyter/index.html#installation) instructions.
 
 Notes:
 - Distiller has only been tested on Ubuntu 16.04 LTS, and with Python 3.5.

docs-src/docs/regularization.md

@@ -50,7 +50,7 @@ Regularization can also be used to induce sparsity.  To induce element-wise spar
 
 \\(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](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.
+[One](https://github.com/NervanaSystems/distiller/blob/master/jupyter/L1-regularization.ipynb) of Distiller's Jupyter 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:

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 23:01:45
+Build Date UTC : 2018-04-28 18:01:09
 -->

docs/install/index.html

@@ -160,7 +160,7 @@
 <p>You may also want to refer to these resources:</p>
 <ul>
 <li><a href="https://github.com/NervanaSystems/distiller#set-up-the-classification-datasets">Dataset installation</a> instructions.</li>
-<li><a href="https://nervanasystems.github.io/distiller/jupyter/index.html#installation">Jupiter installation</a> instructions.</li>
+<li><a href="https://nervanasystems.github.io/distiller/jupyter/index.html#installation">Jupyter installation</a> instructions.</li>
 </ul>
 <p>Notes:
 - Distiller has only been tested on Ubuntu 16.04 LTS, and with Python 3.5.

docs/regularization/index.html

@@ -203,7 +203,7 @@ for input, target in dataset:
 \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="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><a href="https://github.com/NervanaSystems/distiller/blob/master/jupyter/L1-regularization.ipynb">One</a> of Distiller's Jupyter 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

docs/sitemap.xml

@@ -4,7 +4,7 @@
     
     <url>
      <loc>/index.html</loc>
-     <lastmod>2018-04-25</lastmod>
+     <lastmod>2018-04-28</lastmod>
      <changefreq>daily</changefreq>
     </url>
     
@@ -12,7 +12,7 @@
     
     <url>
      <loc>/install/index.html</loc>
-     <lastmod>2018-04-25</lastmod>
+     <lastmod>2018-04-28</lastmod>
      <changefreq>daily</changefreq>
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@@ -20,7 +20,7 @@
     
     <url>
      <loc>/usage/index.html</loc>
-     <lastmod>2018-04-25</lastmod>
+     <lastmod>2018-04-28</lastmod>
      <changefreq>daily</changefreq>
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@@ -28,7 +28,7 @@
     
     <url>
      <loc>/schedule/index.html</loc>
-     <lastmod>2018-04-25</lastmod>
+     <lastmod>2018-04-28</lastmod>
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@@ -37,19 +37,19 @@
         
     <url>
      <loc>/pruning/index.html</loc>
-     <lastmod>2018-04-25</lastmod>
+     <lastmod>2018-04-28</lastmod>
      <changefreq>daily</changefreq>
     </url>
         
     <url>
      <loc>/regularization/index.html</loc>
-     <lastmod>2018-04-25</lastmod>
+     <lastmod>2018-04-28</lastmod>
      <changefreq>daily</changefreq>
     </url>
         
     <url>
      <loc>/quantization/index.html</loc>
-     <lastmod>2018-04-25</lastmod>
+     <lastmod>2018-04-28</lastmod>
      <changefreq>daily</changefreq>
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@@ -58,7 +58,7 @@
     
     <url>
      <loc>/algorithms/index.html</loc>
-     <lastmod>2018-04-25</lastmod>
+     <lastmod>2018-04-28</lastmod>
      <changefreq>daily</changefreq>
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@@ -66,7 +66,7 @@
     
     <url>
      <loc>/model_zoo/index.html</loc>
-     <lastmod>2018-04-25</lastmod>
+     <lastmod>2018-04-28</lastmod>
      <changefreq>daily</changefreq>
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@@ -74,7 +74,7 @@
     
     <url>
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-     <lastmod>2018-04-25</lastmod>
+     <lastmod>2018-04-28</lastmod>
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@@ -82,7 +82,7 @@
     
     <url>
      <loc>/design/index.html</loc>
-     <lastmod>2018-04-25</lastmod>
+     <lastmod>2018-04-28</lastmod>
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