Circular neural Network (RNN, recurrent neural Networks) entry must be learned articles

http://colah.github.io/posts/2015-08-Understanding-LSTMs/

http://www.csdn.net/article/2015-11-25/2826323

Cyclic neural networks (recurrent neural networks,rnns) have been successful and widely used in many natural language processing (Natural Language processing, NLP). However, there are few learning materials related to Rnns online, so this series is to introduce the principle of rnns and how to achieve it. Mainly divided into the following sections to introduce the Rnns:
1. The basic introduction of Rnns and some common Rnns (the content of this article);
2. Describe in detail some of the training algorithms that are often used in Rnns, such as back propagation through Time (BPTT), real-time recurrent Learning (Rtrl), Extended Kalman Filter (EKF) and other learning algorithms, and gradient vanishing problem (vanishing gradient problem)
3. A detailed introduction to long short-term Memory (lstm, length and duration memory network);
4. Introduce clockwork Rnns (Cw-rnns, clock frequency drive cycle neural network) in detail;
5. Implement Rnns based on Python and Theano, including some common Rnns models.

Multilayer Feedback RNN (recurrent neural Network, cyclic neural network) is a kind of artificial neural network with node-directed connection into ring. The internal state of the network can show the dynamic timing behavior. Unlike Feedforward neural networks, RNN can use its internal memory to handle arbitrary sequential input sequences, which makes it easier to handle handwriting recognition, speech recognition, and so on without fragmentation.

Full recursive network (fully recurrent network)
Hopfield Network (Hopfield Network)
Elman Networks and Jordan networks
echo Status Network (Echo State network)
Long-Length Memory network (short term memery network)
Bi-directional Network (bi-directional RNN)
Persistent network (Continuous-time RNN)
Layered RNN (Hierarchical RNN)
Recurrent multilayer perceptron (recurrent multilayer perceptron)
Second-order Recurrent neural Network (Second) Recurrent neural Network)
Pollack's Continuous Cascade network (Pollack ' s sequential cascaded networks)

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