Jimmy


  0975-853-078

  New Taipei City, Taiwan

Currently working as an intelligent manufacturing engineer at AUO, responsible for production line intelligent related projects, defect detection and daily function establishment. I  participated in Tibame AI engineer training and Coursera course learning(IBM, Deeplearning.AI) before. Since learning, I have been using online resources to learn. I have good learning ability. I like logical thinking and programming, and even fall into it.

Have good self-learning ability, bring a pleasant working atmosphere to the team in the process of teamwork, like to share and discuss technology with others, cultivate their own Code Review, and hope to become a leader in the field of programming.

  [email protected]

  https://github.com/Jimmy-frog


Work Experience

Smart Manufacturing Engineer  •  AUO Company

Sep  2021 - Present

1. Familiar with the production line process and on-duty operations from nothing for two months
2. Manage the progress of AI projects
3. Programing for AI Manufacturing project


Education

2018 - 2020

National Taipei University of Technology

Cognitive Ergonomics

2014 - 2018

Ming Chi University Of Technology

Industrial Engineering and Management

Skill

Development Tools


  • Python
  • Jupyter
  • Linus(Ubuntu)
  • Vscode
  • PyCharm
  • Colab

Web crawler


  • Requests
  • Selenium
  • BeatifulSoup

Database


  • MySQL

Data Analysis


  • Pandas
  • Numpy
  • Matplotlib
  • Seaborn
  • Streamlit

Deep Learning


  • Tensorflow
  • Keras
  • Pytorch

Machine Learning


  • SVM
  • LinearRegression
  • RandomForest
  • XGBoost

Cloud Service


  • GCP

Computer Vision


  • OpenCV

  • Yolo

Other


  • Git
  • Github
  • Docker
  • Line Chatbot

Project


Mask Detection


Technology : YOLOv3, YOLOv5,  OpenCV

Data : Kaggle  Database

Process :

In this case,  use yolov3 and yolov5 for training, understanding infrastructure and parameter adjustment


Breast Cancer


Technology :  pandas, numpy, matplotlib, sklearn, xgboost

Data : Kaggle  Database

Process  : 

Use svm, RandomForest and xgboost for training, most people say using xgboost can get best score in test, but not all training data are like this. In this case I got the best score from SVM.


Data Analysis Web


Technology : Streamlit, pandas, sklearn

Data : Kaggle  Database

Process :

This case is under continuous development. I hope that it can be automatically cleaned up, and choose best model and visualization can be analyzed.

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