Update README.md

README.md

@@ -43,6 +43,9 @@ 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
+- [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
 - [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').
@@ -71,6 +74,7 @@ The following examples are coming from [TFLearn](https://github.com/tflearn/tfle
 - [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.
 
 ## 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.