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jd509 / USC-AME-505-ID3-Intelligent-Defect-Detection-System

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The project was a part of the AME 505 course at USC. the objective of the project is to create an application to defect steel surface defects using supervised and unsupervised learning methods. The team comprises of: Aditi Bhagwat, Bharat Deshkulkarni, Jaineel Desai, Kaitlyn Holmstrom, Omey Manyar and Shahwaz Khan.

image-processing supervised-learning image-classification convolutional-neural-networks steel-defect
  • Updated Aug 24, 2021
  • Python

hotaki-lab / hotaki

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Welcome to my simple digital portfolio. This website briefly showcases the breadth and depth level of knowledge and experience in the DS field. The projects consist of academic research and industry standard style projects related to DS field. Please check it out and your feedback is welcome. Thanks.

jekyll jekyll-theme machine-learning github-page fuel datascience research-project results carbon-emissions churn-prediction academia telecommunications patient-data bridges steel-defect
  • Updated Sep 19, 2023
  • HTML

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