Update README.md

Author: aymericdamien

README.md

@@ -43,18 +43,18 @@ Official Website: [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb
 ## More Examples
 The following examples are coming from [TFLearn](https://github.com/tflearn/tflearn), a library that provides a simplified interface for TensorFlow. You can have a look, there are many [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations and layers](http://tflearn.org/doc_index/#api).
 
-## Tutorials
+### Tutorials
 - [TFLearn Quickstart](intro/quickstart.md). Learn the basics of TFLearn through a concrete machine learning task. Build and train a deep neural network classifier.
 
-## Basics
+### Basics
 - [Linear Regression](https://github.com/tflearn/tflearn/blob/master/examples/basics/linear_regression.py). Implement a linear regression using TFLearn.
 - [Logical Operators](https://github.com/tflearn/tflearn/blob/master/examples/basics/logical.py). Implement logical operators with TFLearn (also includes a usage of 'merge').
 - [Weights Persistence](https://github.com/tflearn/tflearn/blob/master/examples/basics/weights_persistence.py). Save and Restore a model.
 - [Fine-Tuning](https://github.com/tflearn/tflearn/blob/master/examples/basics/finetuning.py). Fine-Tune a pre-trained model on a new task.
 - [Using HDF5](https://github.com/tflearn/tflearn/blob/master/examples/basics/use_hdf5.py). Use HDF5 to handle large datasets.
 - [Using DASK](https://github.com/tflearn/tflearn/blob/master/examples/basics/use_dask.py). Use DASK to handle large datasets.
 
-## Computer Vision
+### Computer Vision
 - [Multi-layer perceptron](https://github.com/tflearn/tflearn/blob/master/examples/images/dnn.py). A multi-layer perceptron implementation for MNIST classification task.
 - [Convolutional Network (MNIST)](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_mnist.py). A Convolutional neural network implementation for classifying MNIST dataset.
 - [Convolutional Network (CIFAR-10)](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_cifar10.py). A Convolutional neural network implementation for classifying CIFAR-10 dataset.
@@ -64,31 +64,37 @@ The following examples are coming from [TFLearn](https://github.com/tflearn/tfle
 - [RNN Pixels](https://github.com/tflearn/tflearn/blob/master/examples/images/rnn_pixels.py). Use RNN (over sequence of pixels) to classify images.
 - [Highway Network](https://github.com/tflearn/tflearn/blob/master/examples/images/highway_dnn.py). Highway Network implementation for classifying MNIST dataset.
 - [Highway Convolutional Network](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_highway_mnist.py). Highway Convolutional Network implementation for classifying MNIST dataset.
-- [Residual Network (CIFAR-10)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_cifar10.py). A residual network with shallow bottlenecks applied to CIFAR-10 classification task.
-- [Residual Network (MNIST)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_mnist.py). A residual network with deep bottlenecks applied to MNIST classification task.
+- [Residual Network (MNIST)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_mnist.py). A bottleneck residual network applied to MNIST classification task.
+- [Residual Network (CIFAR-10)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_cifar10.py). A residual network applied to CIFAR-10 classification task.
+- [Google Inception (v3)](https://github.com/tflearn/tflearn/blob/master/examples/images/googlenet.py). Google's Inception v3 network applied to Oxford Flowers 17 classification task.
 - [Auto Encoder](https://github.com/tflearn/tflearn/blob/master/examples/images/autoencoder.py). An auto encoder applied to MNIST handwritten digits.
 
-## Natural Language Processing
+### Natural Language Processing
 - [Recurrent Neural Network (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm.py). Apply an LSTM to IMDB sentiment dataset classification task.
 - [Bi-Directional RNN (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/bidirectional_lstm.py). Apply a bi-directional LSTM to IMDB sentiment dataset classification task.
 - [Dynamic RNN (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/dynamic_lstm.py). Apply a dynamic LSTM to classify variable length text from IMDB dataset.
 - [City Name Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_cityname.py). Generates new US-cities name, using LSTM network.
 - [Shakespeare Scripts Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_shakespeare.py). Generates new Shakespeare scripts, using LSTM network.
 - [Seq2seq](https://github.com/tflearn/tflearn/blob/master/examples/nlp/seq2seq_example.py). Pedagogical example of seq2seq reccurent network. See [this repo](https://github.com/ichuang/tflearn_seq2seq) for full instructions.
+- [CNN Seq](https://github.com/tflearn/tflearn/blob/master/examples/nlp/cnn_sequence_classification.py). Apply a 1-D convolutional network to classify sequence of words from IMDB sentiment dataset.
 
-## Reinforcement Learning
-- [Atari Pacman 1-step Q-Learning](https://github.com/tflearn/tflearn/blob/master/examples/reinforcement_learning/atari_1step_qlearning.py). Teach a machine to play Atari Pacman game using 1-step Q-learning.
+### Reinforcement Learning
+- [Atari Pacman 1-step Q-Learning](https://github.com/tflearn/tflearn/blob/master/examples/reinforcement_learning/atari_1step_qlearning.py). Teach a machine to play Atari games (Pacman by default) using 1-step Q-learning.
 
-## Notebooks
+### Others
+- [Recommender - Wide & Deep Network](https://github.com/tflearn/tflearn/blob/master/examples/others/recommender_wide_and_deep.py). Pedagogical example of wide & deep networks for recommender systems.
+
+### Notebooks
 - [Spiral Classification Problem](https://github.com/tflearn/tflearn/blob/master/examples/notebooks/spiral.ipynb). TFLearn implementation of spiral classification problem from Stanford CS231n.
 
-## Extending TensorFlow
+### Extending TensorFlow
 - [Layers](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/layers.py). Use TFLearn layers along with TensorFlow.
 - [Trainer](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/trainer.py). Use TFLearn trainer class to train any TensorFlow graph.
 - [Built-in Ops](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/builtin_ops.py). Use TFLearn built-in operations along with TensorFlow.
 - [Summaries](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/summaries.py). Use TFLearn summarizers along with TensorFlow.
 - [Variables](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/variables.py). Use TFLearn variables along with TensorFlow.
 
+
 ## Dependencies
 ```
 tensorflow