{"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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錘子簡歷
夢想每個人都有,但不是每個人都有勇氣去堅信,我有!
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教育背景
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2014.09-2018.06
錘子簡歷大學
軟件工程
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工作經(jīng)驗
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2018.12-2019.11
錘子簡歷公司
自然語言處理算法工程師
- 負責 SVD 降維,對大矩陣相乘做優(yōu)化,使用 Numpy 里的 linalg 模塊;
- 負責模糊問題數(shù)據(jù)與規(guī)則建設,Ivr 場景,主要對業(yè)務相關的數(shù)據(jù)做反問交互三要素標注(domain,intent,modifier)用于槽位填充;
- 負責發(fā)現(xiàn)文本數(shù)據(jù)規(guī)律,利用正則表達式設計規(guī)則,訴求拆分,用于 querry 意圖理解(文本分類),批量處理 json 數(shù)據(jù),轉換成文本分類所需要的格式;
- 負責對話系統(tǒng)測評,構建對話系統(tǒng),使用雙層 LSTM Encoder 模型與 Glove Embedding,實現(xiàn) baseline;
- 負責 DSTC8 模型開發(fā),針對已構建的 baseline 進行優(yōu)化;
2018.06-2018.12
錘子簡歷公司
自然語言處理算法工程師
- 負責公司 NLP 算法平臺新功能的研究與應用開發(fā),主要涉及語境探索(文本聚類)、知識挖掘(文本分類)、原因挖掘(TextRank 關鍵詞抽?。┑裙δ埽?/li>
- 負責 nlp 方向新技術和新模型的研究與應用,包括聚類算法 Ameans、AFKMc2,分類算法 LinearSVM、charCNN 等,并嘗試與團隊當前的業(yè)務相結合;
- 參與并開發(fā)部門基于 Lucene 的文本分析引擎的部分功能和相關查詢算法、索引算法;
- 學習相關的技術知識,參加崗位職能培訓,提升自己的技能;
- 完成領導交代的其他工作,有較強的責任心;
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自我評價
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職業(yè)目標/目標崗位:數(shù)據(jù)分析,數(shù)據(jù)挖掘,機器學習
技能:熟練掌握:Oracle,mysql,SQL,python,Shell;R,nosql;常見數(shù)據(jù)挖掘算法(sklearn)。tensorflow,mxnet
通過證書:證券從業(yè)資格,期貨從業(yè)資格,基金從業(yè)資格
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作品展示
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+(支持jpg/png格式,單張圖片不超過2M,最多支持添加8張圖片)
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其他
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- 技能: 熟練掌握python,有面向?qū)ο笏枷?,熟悉Restful Api風格開發(fā),有良好的代碼書寫規(guī)范;熟練使用urllib、requests庫,scrapy框架、scrapy-redis分布式爬蟲;熟練掌握多進程、多線程爬蟲、定時爬蟲、增量爬蟲、可配置爬蟲、掌握BloomFilter優(yōu)化scarpy-redis去重、數(shù)據(jù)的清洗、入庫;熟練使用XPath、BeautifulSoup、Css選擇器等網(wǎng)頁抽取技術;熟練掌握selenium模擬技術;熟悉常見的反爬蟲策略,如:UA檢測,封ip,ajax動態(tài)加載,驗證碼,字體加密,自動化工具檢測;熟悉linu****臺軟件開發(fā),熟悉linux指令,熟悉nginx代理服務器;熟悉HTTP/HTTPS、TCP/IP等網(wǎng)絡協(xié)議;熟練掌握MySQL、MongoDB、sqlite、Redis等數(shù)據(jù)庫的使用;熟練使用flask、Django框架,熟悉MVT/MVC設計模式;熟悉HTML、CSS、Bootstrap、Ajax等前端技術,了解react框架;掌握numpy、pandas、pyecharts等第三方庫;掌握unittest單元測試;熟練使用git代碼管理工具;
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項目經(jīng)歷
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2018.09-2018.12
語境探索(產(chǎn)品:語音大數(shù)據(jù))
- 從客服與客戶的對話文本中提取客戶提出話術、提取文本中重要主題、分析指定關鍵詞或角色的對話語境、展示關鍵詞附近的多條上下文;
- 負責設計 AI 產(chǎn)品原型方案,實現(xiàn)以分詞結果展示的 Demo 版本,設計基于關鍵詞挖掘的新的方案,每個節(jié)點以完整的原文句子顯示這個類別;
- 實現(xiàn) 2018 年論文 A-means 聚類迭代加速算法,在大數(shù)據(jù)情況下提高聚類迭代的速度和效率;