└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Awesome-Code 2 | - Links to a curated list of awesome implementations of neural network models.（tensorflow,torch,theano,keras,...） 3 | - Mainly Question Answering,Machine comprehension,Sentiment Analysis... 4 | - Contributions are welcomed. 5 | 6 | ##Table of Contents 7 | - [Python](#python) 8 | - [Tensorflow](#tensorflow) 9 | - [Theano](#theano) 10 | - [Keras](#keras) 11 | - [Torch](#torch) 12 | - [Matlab](#matlab) 13 | - [Deep Reinforcement Learning](#Deep Reinforcement Learning) 14 | 15 | 16 | ##Python 17 | - [context2vec: Learning Generic Context Embedding with Bidirectional LSTM](https://github.com/orenmel/context2vec) 18 | - [Deep Unordered Composition Rivals Syntactic Methods for Text Classification(Deep Averaging Networks ACL2015)](https://github.com/miyyer/dan) 19 | 20 | 21 | 22 | ##Tensorflow 23 | - [Neural Turing Machine(NMT)](https://github.com/carpedm20/NTM-tensorflow).Taehoon Kim’s(Tensorflow) 24 | - [Neural Turing Machine(NMT)](https://github.com/kaishengtai/torch-ntm). Kai Sheng Tai’s (Torch) 25 | - [Neural Turing Machine(NMT)](https://github.com/shawntan/neural-turing-machines)Shawn Tan’s (Thenao) 26 | - [Neural Turing Machine(NMT)](https://github.com/fumin/ntm)Fumin’s (Go) 27 | - [Neural Turing Machine(NMT)](https://github.com/snipsco/ntm-lasagne)Snip’s (Lasagne) 28 | - [Neural GPUs Learn Algorithms](https://github.com/tensorflow/models/tree/master/neural_gpu) 29 | - [A Neural Attention Model for Abstractive Summarization](https://github.com/BinbinBian/neural-summary-tensorflow) 30 | - [Recurrent Convolutional Memory Network](https://github.com/carpedm20/RCMN) 31 | - [End-To-End Memory Network](https://github.com/carpedm20/MemN2N-tensorflow)@carpedm20 32 | - [End-To-End Memory Network](https://github.com/domluna/memn2n)@domluna 33 | - [Neural Variational Inference for Text Processing](https://github.com/carpedm20/variational-text-tensorflow)---[wikiQA Corpus]() 34 | - [Word2Vec](https://github.com/carpedm20/word2vec-tensorflow) 35 | - [CNN code for insurance QA(question Answer matching)](https://github.com/BinbinBian/insuranceQA-cnn)---[InsuranceQA Corpus](https://github.com/shuzi/insuranceQA) 36 | - [Some experiments on MovieQA with Hsieh,Tom and Huang in AMLDS](https://github.com/YCKung/MovieQA) 37 | - [Teaching Machines to Read and Comprehend](https://github.com/carpedm20/attentive-reader-tensorflow) 38 | - [Convolutional Neural Networks for Sentence Classification (kIM.EMNLP2014)](https://github.com/dennybritz/cnn-text-classification-tf)Tensorflow 39 | - [Convolutional Neural Networks for Sentence Classification (kIM.EMNLP2014)](https://github.com/yoonkim/CNN_sentence)Theano 40 | - [Separating Answers from Queries for Neural Reading Comprehension](https://github.com/dirkweissenborn/qa_network) 41 | - [Neural Associative Memory for Dual-Sequence Modeling](https://github.com/dirkweissenborn/dual_am_rnn) 42 | - [The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems.](https://github.com/dennybritz/chatbot-retrieval) 43 | - [Key-Value Memory Networks for Directly Reading Documents](https://github.com/siyuanzhao/key-value-memory-networks) 44 | - [A statistical natural language generator for spoken dialogue systems(SIGDIAL 2016 short paper)](https://github.com/UFAL-DSG/tgen) 45 | 46 | 47 | ##Theano 48 | - [ End-To-End Memory Networks, formerly known as Weakly Supervised Memory Networks](https://github.com/npow/MemN2N) 49 | - [Memory Networks](https://github.com/npow/MemNN) 50 | - [Dynamic Memory Networks](https://github.com/swstarlab/DynamicMemoryNetworks) 51 | - [Ask Me Anything: Dynamic Memory Networks for Natural Language Processing](https://github.com/YerevaNN/Dynamic-memory-networks-in-Theano)YerevaNN’s (Theano) 52 | - [Memory Networks](https://github.com/facebook/MemNN)Facebook’s (Torch/Matlab) 53 | - [Recurrent Neural Networks with External Memory for Language Understanding](https://github.com/npow/RNN-EM) 54 | - [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]() 55 | - [character-level language models](https://github.com/lipiji/rnn-theano) 56 | - [Hierarchical Encoder-Decoder](https://github.com/BinbinBian/hierarchical-encoder-decoder) 57 | - [A Recurrent Latent Variable Model for Sequential Data](https://github.com/jych/nips2015_vrnn) 58 | - [A Fast Unified Model for Sentence Parsing and Understanding(Stack-augmented Parser-Interpreter Neural Network)](https://github.com/stanfordnlp/spinn) 59 | - [ Semi-supervised Question Retrieval with Gated Convolutions. NAACL 2016](https://github.com/taolei87/rcnn) 60 | - [ Molding CNNs for text: non-linear, non-consecutive convolutions. EMNLP 2015](https://github.com/taolei87/rcnn) 61 | - [Tree RNNs](https://github.com/ofirnachum/tree_rnn) 62 | - [A Character-Level Decoder without Explicit Segmentation for Neural Machine Translation(ACL2016)](https://github.com/nyu-dl/dl4mt-cdec) 63 | - [Charagram: Embedding Words and Sentences via Character n-grams](https://github.com/jwieting/charagram) 64 | - [Towards Universal Paraphrastic Sentence Embeddings](https://github.com/jwieting/iclr2016) 65 | - [Dependency-based Convolutional Neural Networks for Sentence Embedding](https://github.com/cosmmb/DCNN) 66 | - [Siamese-LSTM - Siamese Recurrent Neural network with LSTM for evaluating semantic similarity between sentences.（AAAI2016））](https://github.com/aditya1503/Siamese-LSTM) 67 | 68 | 69 | ##Keras 70 | - [Learning text representation using recurrent convolutional neural network with highway layers](https://github.com/wenying45/deep_learning_tutorial/tree/master/rcnn-hw) 71 | 72 | 73 | ##Torch 74 | - [Sequence-to-sequence model with LSTM encoder/decoders and attention](https://github.com/harvardnlp/seq2seq-attn) 75 | - [Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks](https://github.com/rajarshd/ChainsOfReasoning/tree/master/model) 76 | - [Recurrent Memory Network for Language Modeling](https://github.com/ketranm/RMN) 77 | - [Bag of Tricks for Efficient Text Classification.(FastText)](https://github.com/kemaswill/fasttext_torch) 78 | - [Bag of Tricks for Efficient Text Classification.(FastText)](https://github.com/facebookresearch/fastText)Facebook C++ 79 | - [Character-Aware Neural Language Models (AAAI 2016).](https://github.com/yoonkim/lstm-char-cnn) 80 | - [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks(Tree-LSTM)](https://github.com/stanfordnlp/treelstm) 81 | - [A Neural Attention Model for Abstractive Summarization.](https://github.com/facebook/NAMAS) 82 | - [Text Understanding with the Attention Sum Reader Network, Kadlec et al., ACL 2016.](https://github.com/ganeshjawahar/torch-teacher) 83 | - [A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task, Chen et al., ACL 2016.](https://github.com/ganeshjawahar/torch-teacher) 84 | - [The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations, Hill et al., ICLR 2016.](https://github.com/ganeshjawahar/torch-teacher) 85 | 86 | 87 | ##Matlab 88 | - [When Are Tree Structures Necessary for Deep Learning of Representations](https://github.com/jiweil/Sequence-Models-on-Stanford-Treebank) 89 | 90 | 91 | 92 | ##Deep Reinforcement Learning 93 | 94 | 95 | ##=========================================== 96 | 97 | ##machine learning and deep learning tutorials, articles and other resources 98 | - [machine learning and deep learning tutorials, articles and other resources](https://github.com/ujjwalkarn/Machine-Learning-Tutorials) 99 | - [Knowledge Graph Embeddings including TransE, TransH, TransR and PTransE](https://github.com/thunlp/KG2E) 100 | - [【论文:深度学习NLP的可视化理解】《Visualizing and Understanding Neural Models in NLP》J Li, X Chen, E Hovy, D Jurafsky (2015) ](https://github.com/jiweil/Visualizing-and-Understanding-Neural-Models-in-NLP) 101 | - [Links to the implementations of neural conversational models for different frameworks(seq2seq chatbot links)](https://github.com/nicolas-ivanov/seq2seq_chatbot_links) 102 | - [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning) 103 | - [Awesome Tensorflow Implementations](https://github.com/TensorFlowKR/awesome_tensorflow_implementations) 104 | - [Awesome Recurrent Neural Networks](https://github.com/kjw0612/awesome-rnn) 105 | - [Awesome Reinforcement Learning](https://github.com/aikorea/awesome-rl) 106 | 107 | 108 | 109 | ##People 110 | -[carpedm20](https://github.com/carpedm20) 111 | 112 | --------------------------------------------------------------------------------