Merge pull request arogozhnikov#143 from arogozhnikov/docs-improvements

Docs improvements: wording, visual style, EinMix
Pandinosaurus · Sep 21, 2021 · 66f29c8 · 66f29c8
2 parents 6b7a004 + febe51d
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diff --git a/docs/README.md b/docs/README.md
@@ -2,5 +2,6 @@
 
 - Part 1 notebook at [nbviewer](https://nbviewer.jupyter.org/github/arogozhnikov/einops/blob/master/docs/1-einops-basics.ipynb)
 - Part 2 notebook at [nbviewer](https://nbviewer.jupyter.org/github/arogozhnikov/einops/blob/master/docs/2-einops-for-deep-learning.ipynb)
-- Part 3, writing better code with einops and pytorch: [web link](https://arogozhnikov.github.io/einops/pytorch-examples.html)
+- Part 3: EinMix for great MLPs at [nbviewer](https://nbviewer.jupyter.org/github/arogozhnikov/einops/blob/master/docs/3-einmix-layer.ipynb)
+- Writing better code with einops and pytorch: [web link](https://arogozhnikov.github.io/einops/pytorch-examples.html)
 
diff --git a/docs/css/mkdocs.css b/docs/css/mkdocs.css
@@ -5,4 +5,9 @@ div.highlight{
 
 div.highlight>pre{
     padding: 3px;
-}
+}
+
+.md-typeset ul li, .md-typeset ol li {
+    /*original padding makes lists huge*/
+    margin-bottom: 0.2em;
+}
diff --git a/docs/pages/projects.md b/docs/pages/projects.md
@@ -5,20 +5,20 @@ but it can also help to see einops in action.
 
 Here are some open-source projects that can teach how to leverage einops for your problems
 
-- [lambda networks](https://www.youtube.com/watch?v=3qxJ2WD8p4w) (non-conventional architecture) implementation by [@lucidrains](https://github.com/lucidrains)
-    - nice demonstration how clearer code can be with einops, even compared to verbal description 
-    - <https://github.com/lucidrains/lambda-networks>
+- lambda networks (non-conventional architecture) implemented by [@lucidrains](https://github.com/lucidrains)
+    - nice demonstration how clearer code can be with einops, even compared to description in the paper 
+    - [implementation](https://github.com/lucidrains/lambda-networks) and [video](https://www.youtube.com/watch?v=3qxJ2WD8p4w)
 
 
 - capsule networks (aka capsnets) [implemented in einops](https://github.com/arogozhnikov/readable_capsnet)
     - blazingly fast, concise, and 3x-more memory efficient capsule networks, written with einops  
 
 
 - @lucidrains has a dramatic [collection of vision transformers](https://github.com/lucidrains/vit-pytorch)
-    - there is a plenty of good examples how to use einops efficiently
+    - there is a plenty of good examples how to use einops efficiently in your projects
 
 
-- [NuX](https://github.com/Information-Fusion-Lab-Umass/NuX) - normalizing flows in Jax
+- [NuX](https://github.com/Information-Fusion-Lab-Umass/NuX) — normalizing flows in Jax
     - different rearrangement patterns in normalizing flows have nice mapping to einops
 
 
@@ -29,15 +29,18 @@ Here are some open-source projects that can teach how to leverage einops for you
 - For protein folding, see [implementation](https://github.com/lucidrains/invariant-point-attention)
   of invariant point attention from alphafold 2
 
-## Non-official tutorials about einops
+## Community introductions to einops
 
 Tutorial in the AI summer about einops and einsum:
 <https://theaisummer.com/einsum-attention/>
 
 Implementing visual transformer in pytorch:
 <https://towardsdatascience.com/implementing-visualttransformer-in-pytorch-184f9f16f632>
 
-ML TLDR twitter thread on einops:
+Refactoring machine learning code, one of posts in a series is devoted to einops:
+<https://www.paepper.com/blog/posts/refactoring-machine-learning-code-einops/>
+
+ML TLDR thread on einops:
 <https://twitter.com/mlsummaries/status/1400505282543955970>
 
 Book "Deep Reinforcement Learning in Action" by Brandon Brown & Alexander Zai
@@ -71,5 +74,5 @@ contains an introduction into einops in chapter 10.
 ## Related projects:
 
 - [numpy.einsum](https://numpy.org/doc/stable/reference/generated/numpy.einsum.html) &mdash; grand-dad of einops, this operation is now available in all modern DL frameworks 
-- einops in rust language <https://docs.rs/einops/0.1.0/einops>
-- tensorcast in Julia language <https://juliahub.com/ui/Packages/TensorCast>
+- einops in Rust language <https://docs.rs/einops/0.1.0/einops>
+- tensorcast in Julia language <https://juliahub.com/ui/Packages/TensorCast>
diff --git a/docs/pages/testimonials.md b/docs/pages/testimonials.md
@@ -1,16 +1,19 @@
 <style>
 .md-typeset blockquote {
-    background-color: rgba(63,127,127,0.29);
+    background-color: rgba(128, 128, 128, 0.04);
+    border-color: #002ee380;
+    color: #333;
     margin-top: 2.5em;
     margin-bottom: -0.5em;
     margin-right: 3em;
-    color: #333;
+    padding-right: 2em;
 }
 blockquote + p {
     text-align: right;
     padding-right: 5em;
 }
 </style>
+
 Einops was created three years ago, and never hit big ML news.
 However, little by little and step by step it sneaked into every major AI lab.
 

diff --git a/mkdocs.yml b/mkdocs.yml
@@ -13,6 +13,7 @@ nav:
   - Tutorials:
       Einops Basics: 1-einops-basics.ipynb
       Einops for Deep Learning: 2-einops-for-deep-learning.ipynb
+      Einmix for great MLPs: 3-einmix-layer.ipynb
       Pytorch Examples: pytorch-examples.html
   - API Reference:
       asnumpy: api/asnumpy.md