rebuild docs based on latest repos

Author: jenkinsnm

_sources/source/faqs.md.txt

@@ -98,7 +98,7 @@ AVX-512, AVX2, and VNNI. The DeepSparse Engine will automatically utilize the mo
 
 Yes, absolutely. We can run anywhere you have a CPU with x86 instructions, including on bare metal, in the cloud, on-prem, or at the edge. Additionally, our model optimization tools are able to reduce the footprint of models across all architectures. We only guarantee performance in the DeepSparse Engine.
 
-We’d love to hear from users highly interested in ML performance. If you want to chat about your use cases or how others are leveraging the Deep Sparse Platform, [please reach out](feedback@neuralmagic.com). Or simply head over to the [Neural Magic GitHub repo](https://github.com/neuralmagic) and check out our tools.
+We’d love to hear from users highly interested in ML performance. If you want to chat about your use cases or how others are leveraging the Deep Sparse Platform, [please contact us](https://neuralmagic.com/contact/). Or simply head over to the [Neural Magic GitHub repo](https://github.com/neuralmagic) and check out our tools.
 
 **Do you have available solutions or applications on the Microsoft/Azure platform?**
 

_sources/source/getstarted.md.txt

@@ -58,15 +58,15 @@ Our inference engine and model optimization technologies enable companies to use
   </tr>
 </table>
 
-Today, we offer support for convolutional neural network-based computer vision models, specifically classification and object detection model types such as [the models in SparseZoo](https://docs.neuralmagic.com/sparsezoo/models.html).
+Today, we offer support for convolutional neural network-based computer vision models, specifically classification and object detection model types such as [the models in SparseZoo](https://docs.neuralmagic.com/sparsezoo/source/models.html).
 
-We are continuously exploring models to add to our supported [model list](https://docs.neuralmagic.com/sparsezoo/models.html) and SparseZoo including model architectures beyond computer vision. Popular NLP models such as BERT are on the Neural Magic roadmap; [subscribe for updates](http://neuralmagic.com/subscribe).
+We are continuously exploring models to add to our supported [model list](https://docs.neuralmagic.com/sparsezoo/source/models.html) and SparseZoo including model architectures beyond computer vision. Popular NLP models such as BERT are on the Neural Magic roadmap; [subscribe for updates](http://neuralmagic.com/subscribe).
 
 ### Notes
 
 #### PyTorch and ONNX
 
-Sparsify and the DeepSparse Engine inputs are standardized on the ONNX format. PyTorch has native ONNX export and requires fewer steps than other supported frameworks, such as [Keras or TensorFlow](https://docs.neuralmagic.com/sparseml/quicktour.html#exporting-to-onnx). If you have flexibility in frameworks, consider PyTorch to start.
+Sparsify and the DeepSparse Engine inputs are standardized on the ONNX format. PyTorch has native ONNX export and requires fewer steps than other supported frameworks, such as [Keras or TensorFlow](https://docs.neuralmagic.com/sparseml/source/quicktour.html#exporting-to-onnx). If you have flexibility in frameworks, consider PyTorch to start.
 
 #### Model Considerations
 

_static/basic.css

@@ -277,25 +277,25 @@ p.rubric {
     font-weight: bold;
 }
 
-img.align-left, .figure.align-left, object.align-left {
+img.align-left, figure.align-left, .figure.align-left, object.align-left {
     clear: left;
     float: left;
     margin-right: 1em;
 }
 
-img.align-right, .figure.align-right, object.align-right {
+img.align-right, figure.align-right, .figure.align-right, object.align-right {
     clear: right;
     float: right;
     margin-left: 1em;
 }
 
-img.align-center, .figure.align-center, object.align-center {
+img.align-center, figure.align-center, .figure.align-center, object.align-center {
   display: block;
   margin-left: auto;
   margin-right: auto;
 }
 
-img.align-default, .figure.align-default {
+img.align-default, figure.align-default, .figure.align-default {
   display: block;
   margin-left: auto;
   margin-right: auto;
@@ -319,7 +319,8 @@ img.align-default, .figure.align-default {
 
 /* -- sidebars -------------------------------------------------------------- */
 
-div.sidebar {
+div.sidebar,
+aside.sidebar {
     margin: 0 0 0.5em 1em;
     border: 1px solid #ddb;
     padding: 7px;
@@ -377,12 +378,14 @@ div.body p.centered {
 /* -- content of sidebars/topics/admonitions -------------------------------- */
 
 div.sidebar > :last-child,
+aside.sidebar > :last-child,
 div.topic > :last-child,
 div.admonition > :last-child {
     margin-bottom: 0;
 }
 
 div.sidebar::after,
+aside.sidebar::after,
 div.topic::after,
 div.admonition::after,
 blockquote::after {
@@ -455,20 +458,22 @@ td > :last-child {
 
 /* -- figures --------------------------------------------------------------- */
 
-div.figure {
+div.figure, figure {
     margin: 0.5em;
     padding: 0.5em;
 }
 
-div.figure p.caption {
+div.figure p.caption, figcaption {
     padding: 0.3em;
 }
 
-div.figure p.caption span.caption-number {
+div.figure p.caption span.caption-number,
+figcaption span.caption-number {
     font-style: italic;
 }
 
-div.figure p.caption span.caption-text {
+div.figure p.caption span.caption-text,
+figcaption span.caption-text {
 }
 
 /* -- field list styles ----------------------------------------------------- */

_static/doctools.js

@@ -29,9 +29,14 @@ if (!window.console || !console.firebug) {
 
 /**
  * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
  */
 jQuery.urldecode = function(x) {
-  return decodeURIComponent(x).replace(/\+/g, ' ');
+  if (!x) {
+    return x
+  }
+  return decodeURIComponent(x.replace(/\+/g, ' '));
 };
 
 /**