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As an ML engineer at a manufacturing company, you are tasked with building a model to identify defects in products based on images taken at the end of the assembly line. The goal is to preprocess these images with lower computational requirements while efficiently extracting features related to defects. Which approach should you use to build the model?
Exam-Like
A
Reinforcement learning
B
Recommender system
C
Recurrent Neural Networks (RNN)
D
Convolutional Neural Networks (CNN)
Explanation:
Convolutional Neural Networks (CNNs) are the most suitable approach for image classification tasks such as identifying defects in products based on images. CNNs are specifically designed to process and extract features from images with lower computational requirements, making them well-suited for quickly analyzing images at the end of the assembly line. Options A, B, and C do not provide the same level of efficiency or effectiveness for this specific image-based task.
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