Task: Fine-Tuning Convolutional Neural Network for Quality Control

SUMMARY

Are you ready to elevate your machine learning skills and unlock the full potential of your CNN applications?

In this practical, task-based lesson, you’ll get the opportunity to work with real-world textile data to improve your defect detection models. You will learn how to fine-tune pre-trained CNNs for specific defect detection tasks, apply hyperparameter optimization techniques to boost model efficiency and accuracy, and implement regularization strategies to prevent overfitting. This hands-on approach will allow you to gain practical experience in optimizing CNN models for real-world textile quality control, ensuring your models are both effective and reliable in a production environment.

About The Author

Dilek Dustegor is a Professor of Computing Science at the University of Groningen in the Netherlands. She is interested in bridging the gaps between research, development and implementation using AI and automation. She is pursuing research about modeling, design and analysis of large scale / networked systems using Internet of Things (IoT) and Machine Learning (ML) techniques, with a special interest in smart city applications. She is a seasoned educator, and loves using the newest educational technologies for an enhanced learning experience.