Creating a Convolutional Neural Net for Multiclass Image Recognition in Manufacturing
Join Dr. Christopher Rush, Manager of Manufacturing Products at Galorath Incorporated, as he guides you through creating a convolutional neural network (CNN) for multi-class image recognition. Discover how CNNs can predict manufacturing processes from images, aiding cost estimators and engineers in the early stages of product development.
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Key Learnings
- Introduction to CNNs:
Understand their role in computer vision and manufacturing process prediction. - Building a CNN:
Learn the basics of data preparation and model architecture.
Explore techniques to enhance training and prevent overfitting. - Practical Insights:
Gain tips on using popular tools and libraries.
Discover strategies for improving prediction accuracy.
Gain the knowledge to develop and implement your own CNN models for multi-class image recognition.
Dr. Christopher Rush
Dr. Christopher Rush is the Manager of Manufacturing Products at Galorath Incorporated. He began his educational journey with a Bachelor's in Production Engineering from Brunel University. He obtained a Master's degree in IT for Manufacturing and a PhD in Integrating Quantitative and Qualitative Knowledge for Cost Model Engineering from Cranfield University. He joined the Galorath development team in 2002, managing multiple cost model developments including SEER for Manufacturing, SEER-3D, and SEER for CATIA products.