gym0569's repositories
algorithm_qa
左程云老师算法最优解Python实现
asr-server
FastCGI support for Kaldi ASR
awesome-nlprojects
List of projects related to Natural Language Processing (NLP) that make a geek smile for they exist
awesome-persian-nlp-ir
Curated List of Persian Natural Language Processing and Information Retrieval Tools and Resources
awesome-rl-nlp
Curated Reinforcement Learning Resources for Natural Language Processing
Computer-Vision
学习计算机视觉的道路上
DeepQA
My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot
docker-resources
Docker resources collection. docker资源汇总
DPCNN
Deep Pyramid Convolutional Neural Networks for Text Categorization in PyTorch
gensim-data
Data repository for pretrained NLP models and NLP corpora.
hierarchical-attention-networks
Document classification with Hierarchical Attention Networks in TensorFlow. WARNING: project is currently unmaintained, issues will probably not be addressed.
KGQA_HLM
基于知识图谱的《红楼梦》人物关系可视化及问答系统
MRC2018
2018百度机器阅读理解技术竞赛
NeuroNLP2
Deep neural models for core NLP tasks (Pytorch version)
NJU_KBQA
基于知识库的开放域问答系统的相关工作
nlp_tasks
Natural Language Processing Tasks and References
NowCoder-Solutions
牛客网企业编程真题代码
PyTextSentiment
Emotion Detection & Classification of Tweets Using Streaming APIs. [NLTK] [Scikit Learn] [Twitter Streaming API] [Bing API]
python-recsys
A python library for implementing a recommender system
QANet
A Tensorflow implementation of QANet for machine reading comprehension
RecommenderSystem-Paper
This repository includes some papers that I have read or which I think may be very interesting.
SotA-CV
A repository of state-of-the-art deep learning methods in computer vision
tensorflow-generative-model-collections
Collection of generative models in Tensorflow
TOEFL-QA
A question answering dataset for machine comprehension of spoken content
treelstm.pytorch
Tree LSTM implementation in PyTorch
WordSegment
基于词典的正向最大匹配分词算法和基于词典的逆向最大匹配分词算法
zh-NER-TF
A very simple BiLSTM-CRF model for Chinese Named Entity Recognition 中文命名实体识别 (TensorFlow)