PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #111
You are an ML engineer at a manufacturing company. You need to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. You want your mod
The correct answer is D. Convolutional Neural Networks (CNN). Convolutional Neural Networks (CNNs) are the standard architecture for image-based tasks. Their key advantage is the use of shared convolutional filters that slide across the image to detect spatial features (edges, textures, shapes) with far fewer parameters than fully connected
Question
Options
- AReinforcement learning
- BRecommender system
- CRecurrent Neural Networks (RNN)
- DConvolutional Neural Networks (CNN)
How the community answered
(41 responses)- A2% (1)
- B2% (1)
- C5% (2)
- D90% (37)
Explanation
Convolutional Neural Networks (CNNs) are the standard architecture for image-based tasks. Their key advantage is the use of shared convolutional filters that slide across the image to detect spatial features (edges, textures, shapes) with far fewer parameters than fully connected layers-directly satisfying the lower computation requirement. CNNs are also highly efficient at extracting hierarchical visual features relevant to defect detection. RNNs (C) are designed for sequential/temporal data like text or time series, not images. Reinforcement learning (A) requires an agent-environment interaction loop, which is unsuitable here. Recommender systems (B) are for collaborative filtering of user-item preferences, unrelated to image classification.
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