王子瑋 (WANG TZU WEI)'s Projects
🖍️ AOI Sheet Substrate Defect Inspection Software is installed in the AOI machine which can detect many kinds of defects by means of 【Image Processing Algorithm】 and/or 【AI (Deep Learning)】. In addition, this software includes 【Image Sorter for AI Classification】,【IO】,【Camera Control】, 【Light Control】,【TCP/IP】,【Visual Positioning】,【Laser Correction】, etc.
🖍️ Format Converter Tool can convert many kinds of file format to another format, including 【Word to PDF】, 【PDF to Word】, 【Excel to PDF】, 【Merge PDF】, etc.
git-test-3
git-test2
🖍️ This tool allows users to write number and recognize it in real time. The model of handwriting recognition system is based on a deep Convolutional Neural Network (CNN) architecture.
🖍️ This project achieves some functions of image identification for Self-Driving Cars. First, use yolov5 for object detection. Second, image classification for traffic light and traffic sign. Furthermore, the GUI of this project makes it more user-friendly for users to realize the image identification for Self-Driving Cars.
Texture-Paint on Unity.
WANG TZU WEI Portfolio
🖍️ LabelImgTool is a graphical image annotation tool which can label many kinds of object type (ex: Pen, Eraser, Hollow Rectangle, Filled Rectangle, Hollow Circle, Filled Circle, Hollow Ellipse, Filled Ellipse, Rectangle ROI, Irregular Shape ROI) in images. It can be applied to many deep learning fields, including object detection, semantic segmentation, UNet, etc.
Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
🖍️ This project combines multiple operations in Microsoft Azure Cognitive Services into one GUI, including QnA Maker, LUIS, Computer Vision, Custom Vision, Face, Form Recognizer, Text To Speech, Speech To Text and Speech Translation. It's very user-friendly for users to implement any operation mentioned above.
Machine learning and AI are built on mathematical principles like Calculus, Linear Algebra, Probability, Statistics, and Optimization. This course aims to help you learn some essential foundational concepts and the notation used to express them.
In this data science course, you will be given clear explanations of machine learning theory combined with practical scenarios and hands-on experience building, validating, and deploying machine learning models. You will learn how to build and derive insights from these models using Python, and Azure Notebooks.
Udacity Self-Driving Car Engineer Nanodegree projects.
A minimalist Flutter framework for rapidly building Flutter apps.