Update doc, README.md. Doc is online at keras.io.

README.md

@@ -9,6 +9,8 @@ Use Keras if you need a deep learning library that:
 - supports both convolutional networks (for vision) and recurrent networks (for sequence data). As well as combinations of the two. 
 - runs seamlessly on the CPU and the GPU.
 
+Read the documentation at [Keras.io](http://keras.io).
+
 ## Guiding principles
 
 - __Modularity.__ A model is understood as a sequence of standalone, fully-configurable modules that can be plugged together with as little restrictions as possible. In particular, neural layers, cost functions, optimizers, initialization schemes, activation functions and dropout are all standalone modules that you can combine to create new models. 
@@ -174,70 +176,9 @@ This is a 0.0.1 alpha release. Feature scope is limited, and wild bugs may appea
 
 ## Current capabilities
 
-- model architectures:
+For complete coverage of the API, check out [the Keras documentation](http://keras.io).
-    - Sequential (pipeline of layers)
-
-- layers: 
-    - layers.core:
-        - Dense
-        - Dropout
-        - Activation
-        - Embedding
-        - Reshape
-        - Flatten
-        - RepeatVector
-    - layers.convolutional:
-        - Convolution2D
-        - MaxPooling2D
-    - layers.recurrent:
-        - SimpleRNN
-        - SimpleDeepRNN
-        - GRU
-        - LSTM
-    - layers.advanced_activations:
-        - LeakyReLU
-        - PReLU
-    - layers.normalization:
-        - BatchNormalization
-
-- optimizers: 
-    - SGD (supports decay, momentum, Nesterov momentum)
-    - RMSprop
-    - Adagrad
-    - Adadelta
-
-- datasets:
-    - CIFAR10: thumbnail image classification
-    - Reuters: newswire topic classification
-    - IMDB: sentiment classification
-
-- preprocessing:
-    - image:
-        - ImageDataGenerator: realtime image data augmentation and preprocessing (normalization, ZCA whitening)
-        - random_rotation
-        - random_shift
-        - horizontal_flip
-        - vertical_flip
-    - text:
-        - Tokenizer
-        - one_hot
-    - sequence:
-        - pad_sequences
-
-- objectives:
-    - mean_squared_error
-    - mean_absolute_error
-    - hinge
-    - squared_hinge
-    - binary_crossentropy
-    - categorical_crossentropy
-
-- activation functions:
-    softmax, softplus, relu, sigmoid, hard_sigmoid, linear
-
-- initialization functions:
-    normal, uniform, lecun_uniform, orthogonal
 
+A few highlights: convnets, LSTM, GRU, word2vec-style embeddings, PReLU, batch normalization...
 
 ## Installation
 

docs/mkdocs.yml

@@ -5,10 +5,13 @@ repo_url: http://github.com/fchollet/keras
 site_url: /
 theme_dir: theme
 site_description: Documentation for fast and lightweight Keras Deep Learning library.
+include_404: true
+include_search: true
 
 dev_addr: '0.0.0.0:8000'
 google_analytics: ['UA-61785484-1', 'keras.io']
 
+
 pages:
 - [index.md, Home]
 - [documentation.md, Index]

docs/sources/index.md

@@ -28,7 +28,7 @@ Find the code on Github: [fchollet/keras](https://github.com/fchollet/keras).
 
 Keras is licensed under the [MIT license](http://opensource.org/licenses/MIT). 
 
-## Getting started: 30 seconds to Deep Learning with Keras
+## Getting started: 30 seconds to Keras
 
 The core datastructure of Keras is a __model__, a way to organize layers. Here's a sequential model (a linear pile of layers).