brucexia6116

UESTCChengDu

Pinned Repositories

100-Days-Of-ML-Code
100-Days-Of-ML-Code中文版
Language:Jupyter Notebook10
citation-screening
code for semi-automating screening of biomedical citations for systematic reviews.
Language:Python10
cochrane-nlp
files for systematic review automation project
Language:PLSQL10
cochrane-nlp-experiments
Language:Python1 0 00
LSTM-PICO-Detection
Code for LSTM based model for PICO element detection
Language:Python10
PICO_elements_classification
This is the algorithm part of the literature screening auxiliary system.Only the original data set and some relevant information of the experimental results are given here.
2 1 00
PMCID_getter
a small tool for fetching pubmed central (PMC) IDs -- when available -- from either paper titles or PMIDs.
Language:Python10
pubmed-rct
PubMed 200k RCT dataset: a large dataset for sequential sentence classification.
10
pymc-ordinal-regression
Language:Python10
Rationale-CNN-1
This repository implements the model from paper: Rationale-Augmented Convolutional Neural Networks for Text Classification
Language:Python10

brucexia6116's Repositories

brucexia6116/TextClassification-1
Implemented SPAM and HAM mail detection using Multinomial Naïve Bayes and MCAP Logistic Regression with L2 regularization in Python.
Language:Python
brucexia6116/RoB-2.0
Automated Risk of Bias Assessment, Redux (Outcome Specific)
Language:Jupyter Notebook
brucexia6116/zheng-master
非常牛逼的后台关系系统
Language:Java
brucexia6116/attention
some attention implements
Language:Python
brucexia6116/TIANCHI_Project
天池大数据比赛总结
Language:Jupyter Notebook
brucexia6116/ChineseNER
A neural network model for Chinese named entity recognition
Language:Python
brucexia6116/Attention_is_All_You_Need
Language:Python
brucexia6116/multi-class-text-classification-cnn
Classify Kaggle Consumer Finance Complaints into 11 classes. Build the model with CNN (Convolutional Neural Network) and Word Embeddings on Tensorflow.
Language:Python
brucexia6116/Flying-Bird
Language:Python
brucexia6116/Information-Extraction-Chinese
Chinese Named Entity Recognition with IDCNN/biLSTM+CRF, and Relation Extraction with biGRU+2ATT 中文实体识别与关系提取
Language:Python
brucexia6116/tianchi-diabetes-top12
Language:Jupyter Notebook
brucexia6116/zh-NER-TF
A simple BiLSTM-CRF model for Chinese Named Entity Recognition 中文命名实体识别 (TensorFlow)
Language:Python
brucexia6116/Kobe_analysis
对科比生涯数据训练分类模型
Language:Python
brucexia6116/tensorflow_poems
中文古诗自动作诗机器人，屌炸天，基于tensorflow1.4 api，正在积极维护升级中，快star，保持更新！
Language:Python
brucexia6116/NER
基于tensorflow深度学习的中文的命名实体识别
Language:Python
brucexia6116/tutorials
机器学习相关教程
Language:Python
brucexia6116/Chinese-Annotator
Annotator for Chinese Text Corpus (UNDER DEVELOPMENT) 中文文本标注工具
Language:JavaScript
brucexia6116/DeepLearningFlappyBird
Flappy Bird hack using Deep Reinforcement Learning (Deep Q-learning).
Language:Python
brucexia6116/Data-Science-Projects-in-Python
Data-Science-Projects-in-Python
Language:Jupyter Notebook
brucexia6116/Flappy_Bird
Language:Python
brucexia6116/MedicalTreat_Diabetes
天池精准医疗大赛——人工智能辅助糖尿病遗传风险预测
Language:Python
brucexia6116/natural-language-processing
Programming Assignments and Lectures for Stanford's CS 224: Natural Language Processing with Deep Learning
Language:Python
brucexia6116/ML-Experiments
整理记录本人担任课程助教设计的四个机器学习实验，主要涉及简单的线性回归、朴素贝叶斯分类器、支持向量机、CNN做文本分类。内附实验指导书、讲解PPT、参考代码，欢迎各位码友讨论交流。
Language:Python
brucexia6116/PyTorchText
1st Place Solution for Zhihu Machine Learning Challenge . Implementation of various text-classification models.(知乎看山杯第一名解决方案)
Language:Python
brucexia6116/simplified-deeplearning
Simplified implementations of deep learning related works
Language:Jupyter Notebook
brucexia6116/robotreviewer3
Automatic synthesis of RCTs
Language:Python
brucexia6116/rapping-neural-network
Rap song writing recurrent neural network trained on Kanye West's entire discography
Language:Python
brucexia6116/chinese_entity_linking
brucexia6116/42014601CourseProjectofSoftwareEngineering
软件工程课程设计项目/Lab409：基于词典方法和机基于器学习方法的中文情感倾向分析（Web）
Language:Python
brucexia6116/fastText
Library for fast text representation and classification.
Language:HTML