Pinned Repositories
AsaTung.github.io
I'm AsaTung. This Is My Post Website.
CwsPosNerCNNRNNLSTM
Chinese & English Cws Pos Ner Entity Recognition implement using CNN bi-directional lstm and crf model with char embedding.基于字向量的CNN池化双向BiLSTM与CRF模型的网络，可能一体化的完成中文和英文分词，词性标注，实体识别。主要包括原始文本数据，数据转换,训练脚本,预训练模型,可用于序列标注研究.注意：唯一需要实现的逻辑是将用户数据转化为序列模型。分词准确率约为93%，词性标注准确率约为90%，实体标注（在本样本上）约为85%。
keras
Deep Learning for humans
multiagent-particle-envs
Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"
Reinforcement-learning-with-tensorflow
Simple Reinforcement learning tutorials
soga-text-data-mining
宗成庆老师课上的文本数据挖掘大作业
AsaTung's Repositories
AsaTung/AsaTung.github.io
I'm AsaTung. This Is My Post Website.
AsaTung/CwsPosNerCNNRNNLSTM
Chinese & English Cws Pos Ner Entity Recognition implement using CNN bi-directional lstm and crf model with char embedding.基于字向量的CNN池化双向BiLSTM与CRF模型的网络，可能一体化的完成中文和英文分词，词性标注，实体识别。主要包括原始文本数据，数据转换,训练脚本,预训练模型,可用于序列标注研究.注意：唯一需要实现的逻辑是将用户数据转化为序列模型。分词准确率约为93%，词性标注准确率约为90%，实体标注（在本样本上）约为85%。
AsaTung/keras
Deep Learning for humans
AsaTung/multiagent-particle-envs
Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"
AsaTung/Reinforcement-learning-with-tensorflow
Simple Reinforcement learning tutorials
AsaTung/soga-text-data-mining
宗成庆老师课上的文本数据挖掘大作业