- Links to a curated list of awesome implementations of neural network models.（tensorflow,torch,theano,keras,...）
- Mainly Question Answering,Machine comprehension,Sentiment Analysis...
- Contributions are welcomed.
##Table of Contents
##Python
- [context2vec: Learning Generic Context Embedding with Bidirectional LSTM](https://github.com/orenmel/context2vec)
- [Deep Unordered Composition Rivals Syntactic Methods for Text Classification(Deep Averaging Networks ACL2015)](https://github.com/miyyer/dan)
##Tensorflow
- [Neural Turing Machine(NMT)](https://github.com/carpedm20/NTM-tensorflow).Taehoon Kim’s(Tensorflow)
- [Neural Turing Machine(NMT)](https://github.com/kaishengtai/torch-ntm). Kai Sheng Tai’s (Torch)
- [Neural Turing Machine(NMT)](https://github.com/shawntan/neural-turing-machines)Shawn Tan’s (Thenao)
- [Neural Turing Machine(NMT)](https://github.com/fumin/ntm)Fumin’s (Go)
- [Neural Turing Machine(NMT)](https://github.com/snipsco/ntm-lasagne)Snip’s (Lasagne)
- [Neural GPUs Learn Algorithms](https://github.com/tensorflow/models/tree/master/neural_gpu)
- [A Neural Attention Model for Abstractive Summarization](https://github.com/BinbinBian/neural-summary-tensorflow)
- [Recurrent Convolutional Memory Network](https://github.com/carpedm20/RCMN)
- [End-To-End Memory Network](https://github.com/carpedm20/MemN2N-tensorflow)@carpedm20
- [End-To-End Memory Network](https://github.com/domluna/memn2n)@domluna
- [Neural Variational Inference for Text Processing](https://github.com/carpedm20/variational-text-tensorflow)---[wikiQA Corpus]()
- [Word2Vec](https://github.com/carpedm20/word2vec-tensorflow)
- [CNN code for insurance QA(question Answer matching)](https://github.com/BinbinBian/insuranceQA-cnn)---[InsuranceQA Corpus](https://github.com/shuzi/insuranceQA)
- [Some experiments on MovieQA with Hsieh,Tom and Huang in AMLDS](https://github.com/YCKung/MovieQA)
- [Teaching Machines to Read and Comprehend](https://github.com/carpedm20/attentive-reader-tensorflow)
- [Convolutional Neural Networks for Sentence Classification (kIM.EMNLP2014)](https://github.com/dennybritz/cnn-text-classification-tf)Tensorflow
- [Convolutional Neural Networks for Sentence Classification (kIM.EMNLP2014)](https://github.com/yoonkim/CNN_sentence)Theano
- [Separating Answers from Queries for Neural Reading Comprehension](https://github.com/dirkweissenborn/qa_network)
- [Neural Associative Memory for Dual-Sequence Modeling](https://github.com/dirkweissenborn/dual_am_rnn)
- [The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems.](https://github.com/dennybritz/chatbot-retrieval)
- [Key-Value Memory Networks for Directly Reading Documents](https://github.com/siyuanzhao/key-value-memory-networks)
- [A statistical natural language generator for spoken dialogue systems(SIGDIAL 2016 short paper)](https://github.com/UFAL-DSG/tgen)
##Theano
- [ End-To-End Memory Networks, formerly known as Weakly Supervised Memory Networks](https://github.com/npow/MemN2N)
- [Memory Networks](https://github.com/npow/MemNN)
- [Dynamic Memory Networks](https://github.com/swstarlab/DynamicMemoryNetworks)
- [Ask Me Anything: Dynamic Memory Networks for Natural Language Processing](https://github.com/YerevaNN/Dynamic-memory-networks-in-Theano)YerevaNN’s (Theano)
- [Memory Networks](https://github.com/facebook/MemNN)Facebook’s (Torch/Matlab)
- [Recurrent Neural Networks with External Memory for Language Understanding](https://github.com/npow/RNN-EM)
- [Attention Sum Reader model as presented in "Text Comprehension with the Attention Sum Reader Network"](https://github.com/rkadlec/asreader)---[ CNN and Daily Mail news data QA]()
- [character-level language models](https://github.com/lipiji/rnn-theano)
- [Hierarchical Encoder-Decoder](https://github.com/BinbinBian/hierarchical-encoder-decoder)
- [A Recurrent Latent Variable Model for Sequential Data](https://github.com/jych/nips2015_vrnn)
- [A Fast Unified Model for Sentence Parsing and Understanding(Stack-augmented Parser-Interpreter Neural Network)](https://github.com/stanfordnlp/spinn)
- [ Semi-supervised Question Retrieval with Gated Convolutions. NAACL 2016](https://github.com/taolei87/rcnn)
- [ Molding CNNs for text: non-linear, non-consecutive convolutions. EMNLP 2015](https://github.com/taolei87/rcnn)
- [Tree RNNs](https://github.com/ofirnachum/tree_rnn)
- [A Character-Level Decoder without Explicit Segmentation for Neural Machine Translation(ACL2016)](https://github.com/nyu-dl/dl4mt-cdec)
- [Charagram: Embedding Words and Sentences via Character n-grams](https://github.com/jwieting/charagram)
- [Towards Universal Paraphrastic Sentence Embeddings](https://github.com/jwieting/iclr2016)
- [Dependency-based Convolutional Neural Networks for Sentence Embedding](https://github.com/cosmmb/DCNN)
- [Siamese-LSTM - Siamese Recurrent Neural network with LSTM for evaluating semantic similarity between sentences.（AAAI2016））](https://github.com/aditya1503/Siamese-LSTM)
##Keras
- [Learning text representation using recurrent convolutional neural network with highway layers](https://github.com/wenying45/deep_learning_tutorial/tree/master/rcnn-hw)
##Torch
- [Sequence-to-sequence model with LSTM encoder/decoders and attention](https://github.com/harvardnlp/seq2seq-attn)
- [Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks](https://github.com/rajarshd/ChainsOfReasoning/tree/master/model)
- [Recurrent Memory Network for Language Modeling](https://github.com/ketranm/RMN)
- [Bag of Tricks for Efficient Text Classification.(FastText)](https://github.com/kemaswill/fasttext_torch)
- [Bag of Tricks for Efficient Text Classification.(FastText)](https://github.com/facebookresearch/fastText)Facebook C++
- [Character-Aware Neural Language Models (AAAI 2016).](https://github.com/yoonkim/lstm-char-cnn)
- [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks(Tree-LSTM)](https://github.com/stanfordnlp/treelstm)
- [A Neural Attention Model for Abstractive Summarization.](https://github.com/facebook/NAMAS)
- [Text Understanding with the Attention Sum Reader Network, Kadlec et al., ACL 2016.](https://github.com/ganeshjawahar/torch-teacher)
- [A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task, Chen et al., ACL 2016.](https://github.com/ganeshjawahar/torch-teacher)
- [The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations, Hill et al., ICLR 2016.](https://github.com/ganeshjawahar/torch-teacher)
##Matlab
- [When Are Tree Structures Necessary for Deep Learning of Representations](https://github.com/jiweil/Sequence-Models-on-Stanford-Treebank)
##Deep Reinforcement Learning
##===========================================
##machine learning and deep learning tutorials, articles and other resources
##People
-[carpedm20](https://github.com/carpedm20)