CakeResume Talent Search

Skills Language: R, Python . Technical Skills: Web Scraping, Data Cleaning, Visualization, SQL, NLP. Research Skills: Content Analysis (Qualitative), Survey Design, Academic Writing. TOEIC: 885. Work Experience Data Engineer / Research Assistant Center for Survey Research, Research Center for Humanities and Social Sciences, Academia SinicaPresent Research: Develop NLP measurement for research projects, including Sentiment Analysis, LIWC measures. Build Theory-Based NLP Dictionary Development Framework and apply to working projects. Implement network analysis on survey d ata to invest the relationship between issues. Program: Data collection, including YouTube, Twitter, Facebook, TikTok, etc. Build tool

Wen-Hung Wu GitHub Blog Email 我畢業於雲林科技大學資訊工程所。我的論文研究方向為多領域的任務導向繁體中文對話系統，包含了自然語言生成以及強化學習演算法。就讀期間參加了各種NLP相關比賽及專案。除了NLP相關經驗，我還參加了第一屆由Cupoy舉辦

效能(準確率從0.7937提升至。 Project 電商評論-NLP分析 串接付費API管道，定期 收集電商平台上購買者評論內容文本資訊，並經由NLP技術分類評論內容，供AQM人員製作PBI。 CallLog-NLP分析 通過自動分析call log信息， 提升分析歸類的準確性，以便FIR & IFIR有效改善，滿足

刻神經網路的能力 擁有神經網路跨框架開發的經驗，熟悉Tensorflow/Pytorch 轉 Onnx與TensorRT熟悉各式AI影像處理Object Detection及Segmetation方法 熟悉各式NLP自然語言方法 並且擁有豐富的AI落地之經驗國外研討會 IEEE International Conference on Multimedia Workshop(ICMEW) 論文被接受 https://ieeexplore.ieee.org/abstract/document/

to create natural language processing (NLP) AI software to improve 1 hour efficiency per day per person for disease classification, and develop a programming tool for the label team to improve efficiency in image labeling. ‧ Create RESTFUL APIs for AI software landing on server and workstation using Python and Docker. ‧ Managed over 2 medical AI products for QMS approval, and won 3 awards in the 18th National Innovation Award in the AI area. ‧ Collaborated with a cross-function team that includes doctors and engineers for developing NLP and computer vision projects. Skills Programm...

訓練自身的口語表達能力，並加深對數據分析的專業知識，奠定了機器學習相關基礎知識；碩二階段研究主軸為自然語言(NLP)，參與醫院的產學合作，發表基於醫學擴充詞進行國際疾病編碼之多標籤分類論文及參與研討會，善用不同演算法的特

University, 深度學習工程師 deep-learning-engineer, Oct 2017 ~ Sep 2018 實驗室要導入時間序列相關深度學習之先導研究，題目為自然語言處理，利用tensorflow搭建NLP語言模型 (1) 文本分類：利用RNN與CNN進行文本分類。 (2) Word to vector：文字向量轉化 (3) 文本摘要總結：利用sequence to sequence模型架構，輸入經