CakeResume 找人才
最輕量、快速的招募方案，數百家企業的選擇
搜尋履歷，主動聯繫求職者，提升招募效率。
- 瀏覽所有搜尋結果
- 每日可無限次數開啟陌生對話
- 搜尋僅開放付費企業檢視的履歷
- 檢視使用者信箱 & 電話
職場能力評價定義
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.
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.
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.
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.