SkyWalker's repositories
HighPerformanceConcurrentServer
基于C++11、部分C++14/17特性的一个高性能并发httpserver，包括日志、线程池、内存池、定时器、网络io、http、数据库连接等模块。模块间低耦合高内聚，可作为整体也可单独提供服务。对各模块提供单元测试，对httpserver整体提供性能测试。
nmt_seq2seq_tutorial
a basic implement of seq2seq based on tensorflow used for NMT
BILSTM_CRF_Chinese_NER
BiLTSM+CRF序列标注/命名实体识别代码
textclassification_baseline
一个keras和pytorch 实现的 textclassification baselines
AutoPhrase
AutoPhrase: Automated Phrase Mining from Massive Text Corpora
Deep-Learning-21-Examples
《21个项目玩转深度学习———基于TensorFlow的实践详解》配套代码
2019-sohu-competition
:chicken:2019搜狐校园算法大赛。决赛解决方案ppt、实体lgb单模代码
Algorithm-strategy
手撕代码刷题策略
allennlp
An open-source NLP research library, built on PyTorch.
BERT-BiLSTM-CRF-NER
Tensorflow solution of NER task Using BiLSTM-CRF model with Google BERT Fine-tuning And private Server services
C-Web-Server
tiny web-sever
Chinese-clinical-NER
CCKS2019中文命名实体识别任务。从医疗文本中识别疾病和诊断、解剖部位、影像检查、实验室检验、手术和药物6种命名实体。现已实现基于jieba和AC自动机的baseline构建、基于BiLSTM和CRF的序列标住模型构建。bert的部分代码主要源于https://github.com/charles9n/bert-sklearn.git 感谢作者。 模型最终测试集得分0.81，还有较大改进空间。可以当做一个baseline。
Chinese-Medical-Records-Named-Entity-Recognition
基于BiLSTM-CRF网络的中文电子病历命名实体识别
cpp_guide
C++基础 C++面经
CS-Notes
😋 技术面试必备基础知识
Entity-Relation-Extraction
Entity and Relation Extraction Based on TensorFlow and BERT. 基于TensorFlow和BERT的管道式实体及关系抽取，2019语言与智能技术竞赛信息抽取任务解决方案。Schema based Knowledge Extraction, SKE 2019
knowledge-graph
知识图谱-概念与技术
knowledge_graph_demo
This is a demo for a simple knowledge graph.
Lihang
Statistical learning methods, 统计学习方法 [李航] 值得反复读. [笔记, 代码, notebook, 参考文献, Errata, lihang]
MLiA
Machine Learning in Action [Peter Harrington] 机器学习实战, Python3
NLP-progress
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
nndl.github.io
《神经网络与深度学习》 Neural Network and Deep Learning
Tencent-Advertising-Algorithm-competion
腾讯广告算法大赛2020
Tencent2019_Preliminary_Rank1st
The code for 2019 Tencent College Algorithm Contest, and the online result ranks 1st in the preliminary.
zju-icicles
浙江大学课程攻略共享计划