Documentation fixes

README.md

@@ -4,9 +4,10 @@
 
 ## You have just found Keras.
 
-Keras is a minimalist, highly modular neural networks library, written in Python and capable of running either on top of either [TensorFlow](https://github.com/tensorflow/tensorflow) or [Theano](https://github.com/Theano/Theano). It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
+Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either [TensorFlow](https://github.com/tensorflow/tensorflow) or [Theano](https://github.com/Theano/Theano). It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
 
 Use Keras if you need a deep learning library that:
+
 - allows for easy and fast prototyping (through total modularity, minimalism, and extensibility).
 - supports both convolutional networks and recurrent networks, as well as combinations of the two.
 - supports arbitrary connectivity schemes (including multi-input and multi-output training).
@@ -109,6 +110,7 @@ Keras uses the following dependencies:
 - Optional but recommended if you use CNNs: cuDNN.
 
 *When using the Theano backend:*
+
 - Theano
     - [See installation instructions](http://deeplearning.net/software/theano/install.html#install).
 
@@ -118,6 +120,7 @@ sudo pip install git+git://github.com/Theano/Theano.git
 ```
 
 *When using the TensorFlow backend:*
+
 - TensorFlow
     - [See installation instructions](https://github.com/tensorflow/tensorflow#download-and-setup).
 

docs/templates/index.md

@@ -5,6 +5,7 @@
 Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either [TensorFlow](https://github.com/tensorflow/tensorflow) or [Theano](https://github.com/Theano/Theano). It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
 
 Use Keras if you need a deep learning library that:
+
 - allows for easy and fast prototyping (through total modularity, minimalism, and extensibility).
 - supports both convolutional networks and recurrent networks, as well as combinations of the two.
 - supports arbitrary connectivity schemes (including multi-input and multi-output training).
@@ -26,15 +27,15 @@ Keras is compatible with: __Python 2.7-3.5__.
 
 - __Easy extensibility.__ New modules are dead simple to add (as new classes and functions), and existing modules provide ample examples. To be able to easily create new modules allows for total expressiveness, making Keras suitable for advanced research.
 
-- __Work with Python__. No separate model configuration files in a declarative format. Models are described in Python code, which is compact, easier to debug, and allows for ease of extensibility.
+- __Work with Python__. No separate models configuration files in a declarative format. Models are described in Python code, which is compact, easier to debug, and allows for ease of extensibility.
 
 
 ------------------
 
 
 ## Getting started: 30 seconds to Keras
 
-The core data structure of Keras is a __model__, a way to organize layers. There are two types of models: [`Sequential`](/models/#sequential) and [`Graph`](/models/#graph).
+The core datastructure of Keras is a __model__, a way to organize layers. There are two types of models: [`Sequential`](http://keras.io/models/#sequential) and [`Graph`](http://keras.io/models/#graph).
 
 Here's the `Sequential` model (a linear pile of layers):
 
@@ -107,6 +108,7 @@ Keras uses the following dependencies:
 - Optional but recommended if you use CNNs: cuDNN.
 
 *When using the Theano backend:*
+
 - Theano
     - [See installation instructions](http://deeplearning.net/software/theano/install.html#install).
 
@@ -116,6 +118,7 @@ sudo pip install git+git://github.com/Theano/Theano.git
 ```
 
 *When using the TensorFlow backend:*
+
 - TensorFlow
     - [See installation instructions](https://github.com/tensorflow/tensorflow#download-and-setup).