{"m1":["resume_head","resume_name","resume_base_info"],"m2":[],"m3":["resume_job","resume_edu","resume_work","resume_hobby","resume_skill","resume_honor","resume_summary","resume_internship","resume_project","resume_portfolio","30a91618-8bb1-483e-8d9f-efd8d7126e2e","5d62b36b-000f-4281-a8e3-f50f44a54eaf"],"m4":[]}
.resume_main[data_color] .skill_item .skill_slider span::before{background-color:${color};}
.resume_main[data_color] .skill_slider s i{background-color:${relative_skill_color};}
.resume_main[data_color] .skill_style_01.skill_item .skill_slider s {border-color:${relative_skill_color};}
.resume_main[data_color] .skill_style_01.skill_item .skill_slider s i{background-color:${relative_skill_color};}
.resume_main[data_color] .skill_style_04.skill_item .skill_slider[data_level="average"] i,.resume_main[data_color] .skill_style_07.skill_item .skill_slider[data_level="average"] i{box-shadow:24px 0 0 ${relative_skill_color}, 48px 0 0 #ccc, 72px 0 0 #ccc, 96px 0 0 #ccc, 120px 0 0 #ccc;}
.resume_main[data_color] .skill_style_04.skill_item .skill_slider[data_level="good"] i,.resume_main[data_color] .skill_style_07.skill_item .skill_slider[data_level="good"] i{box-shadow:24px 0 0 ${relative_skill_color}, 48px 0 0 ${relative_skill_color}, 72px 0 0 #ccc, 96px 0 0 #ccc, 120px 0 0 #ccc;}
.resume_main[data_color] .skill_style_04.skill_item .skill_slider[data_level="advanced"] i,.resume_main[data_color] .skill_style_07.skill_item .skill_slider[data_level="advanced"] i{box-shadow:24px 0 0 ${relative_skill_color}, 48px 0 0 ${relative_skill_color}, 72px 0 0 ${relative_skill_color}, 96px 0 0 #ccc, 120px 0 0 #ccc;}
.resume_main[data_color] .skill_style_04.skill_item .skill_slider[data_level="expert"] i,.resume_main[data_color] .skill_style_07.skill_item .skill_slider[data_level="expert"] i{box-shadow:24px 0 0 ${relative_skill_color}, 48px 0 0 ${relative_skill_color}, 72px 0 0 ${relative_skill_color}, 96px 0 0 ${relative_skill_color}, 120px 0 0 #ccc;}
.resume_main[data_color] .skill_style_08.skill_item .skill_slider[data_level="average"] i{box-shadow:9px 0 0 ${relative_skill_color}, 18px 0 0 ${relative_skill_color}, 27px 0 0 ${relative_skill_color}, 36px 0 0 ${relative_skill_color}, 45px 0 0 ${relative_skill_color},54px 0 0 #ccc,63px 0 0 #ccc,72px 0 0 #ccc,81px 0 0 #ccc;}
.resume_main[data_color] .skill_style_08.skill_item .skill_slider[data_level="good"] i{box-shadow:9px 0 0 ${relative_skill_color}, 18px 0 0 ${relative_skill_color}, 27px 0 0 ${relative_skill_color}, 36px 0 0 ${relative_skill_color}, 45px 0 0 ${relative_skill_color},54px 0 0 ${relative_skill_color},63px 0 0 #ccc,72px 0 0 #ccc,81px 0 0 #ccc;}
.resume_main[data_color] .skill_style_08.skill_item .skill_slider[data_level="advanced"] i{box-shadow:9px 0 0 ${relative_skill_color}, 18px 0 0 ${relative_skill_color}, 27px 0 0 ${relative_skill_color}, 36px 0 0 ${relative_skill_color}, 45px 0 0 ${relative_skill_color},54px 0 0 ${relative_skill_color},63px 0 0 ${relative_skill_color},72px 0 0 #ccc,81px 0 0 #ccc;}
.resume_main[data_color] .skill_style_08.skill_item .skill_slider[data_level="expert"] i{box-shadow:9px 0 0 ${relative_skill_color}, 18px 0 0 ${relative_skill_color}, 27px 0 0 ${relative_skill_color}, 36px 0 0 ${relative_skill_color}, 45px 0 0 ${relative_skill_color},54px 0 0 ${relative_skill_color},63px 0 0 ${relative_skill_color},72px 0 0 ${relative_skill_color},81px 0 0 #ccc;}
.resume_main[data_color] .hobby_item .hobby_item_con .hobby_item_list a.alifont{border-color:${relative_hobby_color};color:${relative_hobby_color}; }
/* ������ */
.resume_main[data_color] .resume_cover .cover_html svg [data-svg="fill"] {fill:${color};}
.resume_main[data_color] .resume_cover .cover_html svg [data-svg="stroke"] {stroke:${color};}
.resume_main[data_color] .resume_letter .letter_html svg [data-svg="fill"] {fill:${color};}
.resume_main[data_color] .resume_letter .letter_html svg [data-svg="stroke"] {stroke:${color};}
.resume_main[data_color] .resume_letter .letter_html svg [data-fill="fill"] {fill:${color};}
.resume_main[data_color] .resume_cover[data-type="07"] .resume_cover_avatar{border-color: ${color};}
.resume_main[data_color] .resume_cover[data-type="07"] .resume_cover_content{background:${color}}
.resume_main[data_color] .resume_cover[data-type="07"] .cover_item_list a.alifont{color: ${color};}
.resume_main[data_color] .resume_cover[data-type="08"] .resume_cover_content::after{background:${color}}
.resume_main[data_color] .resume_cover[data-type="09"] .resume_cover_content{background:${color}}
.resume_main[data_color] .resume_cover[data-type="09"] .cover_item_list a.alifont{color: ${color};}
.resume_main[data_color] .resume_cover[data-type="10"]{background-color:${color}}
.resume_main[data_color] .resume_cover[data-type="11"] .resume_cover_content{background-color:${color}}
.resume_main[data_color] .resume_cover[data-type="14"]{background-color:${color}}
.resume_main[data_color] .resume_cover[data-type="15"]{background-color:${color}}
.resume_main[data_color] .resume_cover[data-type="19"] .resume_cover_word::before{background-color:${color}}
.resume_main[data_color] .resume_cover[data-type="20"]{background-color:${color}}
.resume_main[data_color] .resume_letter[data-type="06"]{background-color:${color}}
.resume_main[data_color] .resume_letter[data-type="12"]{background-color:${color}}
.resume_main[data_color] .resume_item dl dt span.resume_item_title_span,.resume_main[data_color] .name_item .name-con .name{color:${color};}
.resume_main[data_color] .resume_item dl dt{border-bottom-color:${color};}
.resume_main[data_color] .resume_item dl dt span.resume_item_title_span{color:${color};}
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姓名
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锤子简历
梦想每个人都有,但不是每个人都有勇气去坚信,我有!
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教育背景
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2014.09-2018.06
锤子简历大学
软件工程
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工作经验
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2018.12-2019.11
锤子简历公司
自然语言处理算法工程师
- 负责 SVD 降维,对大矩阵相乘做优化,使用 Numpy 里的 linalg 模块;
- 负责模糊问题数据与规则建设,Ivr 场景,主要对业务相关的数据做反问交互三要素标注(domain,intent,modifier)用于槽位填充;
- 负责发现文本数据规律,利用正则表达式设计规则,诉求拆分,用于 querry 意图理解(文本分类),批量处理 json 数据,转换成文本分类所需要的格式;
- 负责对话系统测评,构建对话系统,使用双层 LSTM Encoder 模型与 Glove Embedding,实现 baseline;
- 负责 DSTC8 模型开发,针对已构建的 baseline 进行优化;
2018.06-2018.12
锤子简历公司
自然语言处理算法工程师
- 负责公司 NLP 算法平台新功能的研究与应用开发,主要涉及语境探索(文本聚类)、知识挖掘(文本分类)、原因挖掘(TextRank 关键词抽取)等功能;
- 负责 nlp 方向新技术和新模型的研究与应用,包括聚类算法 Ameans、AFKMc2,分类算法 LinearSVM、charCNN 等,并尝试与团队当前的业务相结合;
- 参与并开发部门基于 Lucene 的文本分析引擎的部分功能和相关查询算法、索引算法;
- 学习相关的技术知识,参加岗位职能培训,提升自己的技能;
- 完成领导交代的其他工作,有较强的责任心;
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自我评价
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职业目标/目标岗位:数据分析,数据挖掘,机器学习
技能:熟练掌握:Oracle,mysql,SQL,python,Shell;R,nosql;常见数据挖掘算法(sklearn)。tensorflow,mxnet
通过证书:证券从业资格,期货从业资格,基金从业资格
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作品展示
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+(支持jpg/png格式,单张图片不超过2M,最多支持添加8张图片)
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其他
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- 技能: 熟练掌握python,有面向对象思想,熟悉Restful Api风格开发,有良好的代码书写规范;熟练使用urllib、requests库,scrapy框架、scrapy-redis分布式爬虫;熟练掌握多进程、多线程爬虫、定时爬虫、增量爬虫、可配置爬虫、掌握BloomFilter优化scarpy-redis去重、数据的清洗、入库;熟练使用XPath、BeautifulSoup、Css选择器等网页抽取技术;熟练掌握selenium模拟技术;熟悉常见的反爬虫策略,如:UA检测,封ip,ajax动态加载,验证码,字体加密,自动化工具检测;熟悉linu****台软件开发,熟悉linux指令,熟悉nginx代理服务器;熟悉HTTP/HTTPS、TCP/IP等网络协议;熟练掌握MySQL、MongoDB、sqlite、Redis等数据库的使用;熟练使用flask、Django框架,熟悉MVT/MVC设计模式;熟悉HTML、CSS、Bootstrap、Ajax等前端技术,了解react框架;掌握numpy、pandas、pyecharts等第三方库;掌握unittest单元测试;熟练使用git代码管理工具;
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项目经历
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2018.09-2018.12
语境探索(产品:语音大数据)
- 从客服与客户的对话文本中提取客户提出话术、提取文本中重要主题、分析指定关键词或角色的对话语境、展示关键词附近的多条上下文;
- 负责设计 AI 产品原型方案,实现以分词结果展示的 Demo 版本,设计基于关键词挖掘的新的方案,每个节点以完整的原文句子显示这个类别;
- 实现 2018 年论文 A-means 聚类迭代加速算法,在大数据情况下提高聚类迭代的速度和效率;