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hard problems step-by-step. https://linktr.ee/okt.wang 專案經歷 1st place at IEEE ICME 2022 Grand Challenge IEEE ICME 2022 Grand Challenge: Low‑power Deep Learning Semantic Segmentation Model Compression Competition for Traffic Scene in Asian Countries • Training Deep Learning model DDRNet to minimize the power consumption and speed on MediaTek Dimensity 9000 • Implemented Deep Learning model with Pytorch and OpenCV • Deploy PyTorch model to TensorFlow Lite with post-quantization Mar 2022 VR Portable Concussion Diagnosis • Implemented diagnostic machine learning (Scikit‑learn, Pandas)& deep learning algorithm (Pytorch & Keras) • Developed

及達成公司目標，獲得的成就感，也讓我從工作中找到自己的使命感！ Taipei City, Taiwan [email protected] Python TensorFlow Scikit-learn Flask Librosa HTML C /C++ Application Machine learning Deep learning Microcontroller Development Deep learning applied to image processing Office software Word Power Point Excel Development tools VS Code Other Docker Github EducationShih Chien University Department of Food and Beverage Management Work Experience 八月六 月 2022 Sales Manager MasterApp 與工程團隊協作

林科廷 Steven 25歲, 3 餘年軟體開發經驗, 主要專精於 網頁後端 & 機器學習 經歷過 小型接案團隊, 外商純軟新創 & 傳統電子公司 除了 後端 與 AI 之外, 也參與過 前端開發 & DevOp 的部署, 以及 AWS、Azure 雲端服務 操作 五年完成學碩學位(3.5年學士 + 1.5年碩士

架構建立的語碼轉換語音辨識系統，探討在語碼轉換資料的情況下，對特定領域的資料進行調試並達到系統上的改進， 且該論文 已被接受並於研討會上發表。 技能 Skill 程式語言 Ｇood: Python、C Moderate: CSS、Html、JavaScript 其他 Machine Learning: Pytorch、Tensorflow Version Control: Github、Git Containerization: Docker IDE/Editor: Visual Studio、Jupyter Others: Photoshop、Excel

李政旻 [email protected] 曾在一間數位轉型解決方案的公司實習，參與過許多場域數位化的專案，負責整合公司現有的產品，並製作客製化需求。感受到資料分析應用的博大精深，而回到研究所深造，學位論文 研究主題是生物影像辨識，以手掌影像進

Predicti on 2022/10//01/17 ▪Web Crawler an d Data cleaning (Selenium, Regex) Collected raw data on 104, Medium, ithelp, ptt and Dcard, a voiding anti-crawler detection by Undetected ChromeDriver. Built etl infrastructure and delivered data to MySQL database using SQLAlchemy. ▪Machine Learning Model (XGBRegressor, LightGBM, Random Forest Regressor, LGBMRegressor, SVR) Ability of feature engineering and application of Heapmap to reduce the quantity of features. Built models with Cross Validation to avoid overfitting and achieved R SQUARE 0.89, RMSE 6,000. Explained Al model(XAl) Shapley Value to

National Taiwan University, Biomechatronics Engineering~Skill Python | Pytorch | Django | C++ | MySQL | Verilog | C Correlated curriculum Machine Learning , Deep Learning for Computer Vision , Data Structure and Algorithm, Operation System Digital Image Processing, Bioinformatics Algorithms , Biomedical Data Mining, Computer Organization, Digital Systems Design, Digital Signal processor Thesis title：Using deep learning to predict T-cell receptors binding specificity Project Experience C++ Greedy snake design Verilog Gobang in FPGA Single Cycle implementation Python Data structure algorithm implementation Image processing implementation Pytorch Image Classification ( CNN, Semi-Supervised learning, S elf-Supervised learning, VIT, ACGAN ) Sequence to Sequence( Transformer, BERT ) Semantic