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daixl's Projects

bert-bilstm-crf-ner icon bert-bilstm-crf-ner

Tensorflow solution of NER task Using BiLSTM-CRF model with Google BERT Fine-tuning And private Server services

bilibili_notes icon bilibili_notes

攻城狮之家B站课程课件合集,2号仓库地址https://github.com/JokerJohn/bilibli_notes2.git

bilibli_notes2 icon bilibli_notes2

攻城狮之家B站视频课程课件合集2,1号仓库https://github.com/JokerJohn/bilibili_notes.git

customer_satisfaction_analysis icon customer_satisfaction_analysis

基于在线民宿UGC数据的意见挖掘项目,包含数据挖掘和NLP相关的处理,负责数据采集、主题抽取、情感分析等任务。开发的目的是克服用户打分和评论不一致,实时对重庆地区在线民宿的满意度进行评测,使用Python实现了在线评论采集和情感可视化分析。搭建了百度地图POI查询入口,可以进行自动化的批量查询POI信息的功能;构建了基于在线民宿语料的Word2vec主题聚类模型,利用主题中心词能找出对应的主题属性字典;以用户打分作为标注,然后通过实验贝叶斯、SVM、决策树等多种分类模型,最后选用贝叶斯模型进行情感分析,将情感分类概率分布作为情感趋势,最后通过POI热力图的方式对不同地域的民宿满意度进行展示。

d2l-zh icon d2l-zh

《动手学深度学习》,英文版即伯克利深度学习(STAT 157,2019春)教材。面向中文读者、能运行、可讨论。

deeplearning-500-questions icon deeplearning-500-questions

深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为18个章节,近30万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系[email protected] 版权所有,违权必究 Tan 2018.06

fsauor2018 icon fsauor2018

基于LSTM网络与自注意力机制对中文评论进行细粒度情感分析

information-extraction-chinese icon 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

javaguide icon javaguide

【Java学习+面试指南】 一份涵盖大部分Java程序员所需要掌握的核心知识。

leetcode icon leetcode

Play Leetcode with different Programming language

machinelearning icon machinelearning

Machine Learning in Action学习笔记,一个文件夹代表一个算法,每个文件夹包含算法所需的数据集、源码和图片,图片放在pic文件夹中,数据集放在在Data文件夹内。书中的代码是python2的,有不少错误,这里代码是我用python3写的,且都能直接运行

ml-nlp icon ml-nlp

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现,也是作为一个算法工程师必会的理论基础知识。

nlp_basis icon nlp_basis

这是我学习一个NLP教程【2019最新AI 自然语言处理之深度机器学习顶级项目实战课程】做的笔记与代码

pymining icon pymining

A few data mining algorithms in pure python

scikit-opt icon scikit-opt

Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman) 遗传、粒子群、模拟退火、蚁群算法等

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