build based on 7e5669d

latest/apis/backends.html

@@ -150,7 +150,7 @@ Backends
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+          <a class="edit-page" href="https://github.com/MikeInnes/Flux.jl/tree/7e5669d2f60edb9cc56d3498b22999c09a387960/docs/src/apis/backends.md">
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latest/apis/batching.html

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+          <a class="edit-page" href="https://github.com/MikeInnes/Flux.jl/tree/7e5669d2f60edb9cc56d3498b22999c09a387960/docs/src/apis/batching.md">
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latest/apis/storage.html

@@ -139,7 +139,7 @@ Storing Models
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+          <a class="edit-page" href="https://github.com/MikeInnes/Flux.jl/tree/7e5669d2f60edb9cc56d3498b22999c09a387960/docs/src/apis/storage.md">
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latest/contributing.html

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+          <a class="edit-page" href="https://github.com/MikeInnes/Flux.jl/tree/7e5669d2f60edb9cc56d3498b22999c09a387960/docs/src/contributing.md">
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latest/examples/char-rnn.html

@@ -139,7 +139,7 @@ Char RNN
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+          <a class="edit-page" href="https://github.com/MikeInnes/Flux.jl/tree/7e5669d2f60edb9cc56d3498b22999c09a387960/docs/src/examples/char-rnn.md">
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latest/examples/logreg.html

@@ -139,7 +139,7 @@ Simple MNIST
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+          <a class="edit-page" href="https://github.com/MikeInnes/Flux.jl/tree/7e5669d2f60edb9cc56d3498b22999c09a387960/docs/src/examples/logreg.md">
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latest/index.html

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+          <a class="edit-page" href="https://github.com/MikeInnes/Flux.jl/tree/7e5669d2f60edb9cc56d3498b22999c09a387960/docs/src/index.md">
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@@ -167,10 +167,10 @@ Flux
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       <p>
-Flux is a machine learning library, implemented in Julia. In a nutshell, it simply lets you run normal Julia code on a backend like TensorFlow. It also provides many conveniences for doing deep learning.
+Flux is a library for machine learning, implemented in Julia. In a nutshell, it simply lets you run normal Julia code on a backend like TensorFlow. It also provides many conveniences for doing deep learning.
       </p>
       <p>
-This gives you great flexibility. You can use a convenient Keras-like API if you want something simple, but you can also drop down to straight mathematics, or build your own abstractions. You can even use Flux&#39;s utilities (like optimisers) with a completely different backend (like 
+Flux is very flexible. You can use a convenient Keras-like API if you want something simple, but you can also drop down to straight mathematics, or build your own abstractions. You can even use Flux&#39;s utilities (like optimisers) with a completely different backend (like 
         <a href="https://github.com/denizyuret/Knet.jl">
 Knet
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latest/internals.html

@@ -136,7 +136,7 @@ Internals
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latest/models/basics.html

@@ -170,7 +170,7 @@ Model Building Basics
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+          <a class="edit-page" href="https://github.com/MikeInnes/Flux.jl/tree/7e5669d2f60edb9cc56d3498b22999c09a387960/docs/src/models/basics.md">
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@@ -347,7 +347,7 @@ Dressed like a model
       <p>
 We noted above that a model is a function with trainable parameters. Normal functions like 
 <code>exp</code>
- are actually models too, that happen to have 0 parameters. Flux doesn&#39;t care, and anywhere that you use one, you can use the other. For example, 
+ are actually models too – they just happen to have 0 parameters. Flux doesn&#39;t care, and anywhere that you use one, you can use the other. For example, 
 <code>Chain</code>
  will happily work with regular functions:
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latest/models/debugging.html

@@ -139,7 +139,7 @@ Debugging
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+          <a class="edit-page" href="https://github.com/MikeInnes/Flux.jl/tree/7e5669d2f60edb9cc56d3498b22999c09a387960/docs/src/models/debugging.md">
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latest/models/recurrent.html

@@ -139,7 +139,7 @@ Recurrence
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+          <a class="edit-page" href="https://github.com/MikeInnes/Flux.jl/tree/7e5669d2f60edb9cc56d3498b22999c09a387960/docs/src/models/recurrent.md">
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latest/models/templates.html

@@ -155,7 +155,7 @@ Model Templates
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+          <a class="edit-page" href="https://github.com/MikeInnes/Flux.jl/tree/7e5669d2f60edb9cc56d3498b22999c09a387960/docs/src/models/templates.md">
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latest/search_index.js

@@ -13,7 +13,7 @@ var documenterSearchIndex = {"docs": [
     "page": "Home",
     "title": "Flux",
     "category": "section",
-    "text": "... Initialising Photon Beams ...Flux is a machine learning library, implemented in Julia. In a nutshell, it simply lets you run normal Julia code on a backend like TensorFlow. It also provides many conveniences for doing deep learning.This gives you great flexibility. You can use a convenient Keras-like API if you want something simple, but you can also drop down to straight mathematics, or build your own abstractions. You can even use Flux's utilities (like optimisers) with a completely different backend (like Knet) or mix and match approaches.Note that Flux is in alpha. Many things work but the API is still in a state of... well, it might change.Note: If you're using Julia v0.5 please see this version of the docs instead."
+    "text": "... Initialising Photon Beams ...Flux is a library for machine learning, implemented in Julia. In a nutshell, it simply lets you run normal Julia code on a backend like TensorFlow. It also provides many conveniences for doing deep learning.Flux is very flexible. You can use a convenient Keras-like API if you want something simple, but you can also drop down to straight mathematics, or build your own abstractions. You can even use Flux's utilities (like optimisers) with a completely different backend (like Knet) or mix and match approaches.Note that Flux is in alpha. Many things work but the API is still in a state of... well, it might change.Note: If you're using Julia v0.5 please see this version of the docs instead."
 },
 
 {
@@ -93,7 +93,7 @@ var documenterSearchIndex = {"docs": [
     "page": "Model Building Basics",
     "title": "Dressed like a model",
     "category": "section",
-    "text": "We noted above that a model is a function with trainable parameters. Normal functions like exp are actually models too, that happen to have 0 parameters. Flux doesn't care, and anywhere that you use one, you can use the other. For example, Chain will happily work with regular functions:foo = Chain(exp, sum, log)\nfoo([1,2,3]) == 3.408 == log(sum(exp([1,2,3])))"
+    "text": "We noted above that a model is a function with trainable parameters. Normal functions like exp are actually models too – they just happen to have 0 parameters. Flux doesn't care, and anywhere that you use one, you can use the other. For example, Chain will happily work with regular functions:foo = Chain(exp, sum, log)\nfoo([1,2,3]) == 3.408 == log(sum(exp([1,2,3])))"
 },
 
 {