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…

Option A is incorrect because reinforcement learning is not a suitable approach to build a model that

identifies defects in products based on images of the product taken at the end of the assembly

line. Reinforcement learning is a type of machine learning that learns from its own actions and

rewards, rather than from labeled data or explicit feedback1. Reinforcement learning is more

suitable for problems that involve sequential decision making, such as games, robotics, or control

systems1. However, defect detection is a problem that involves image classification or segmentation,

which requires supervised learning, not reinforcement learning.

Option B is incorrect because a recommender system is not a relevant approach to build a model that

identifies defects in products based on images of the product taken at the end of the assembly line. A

recommender system is a system that suggests items or actions to users based on their p

,

behavior, or context2. A recommender system is more suitable for problems that involve

personalization, such as e-commerce, entertainment, or social media2. However, defect detection is

a problem that involves image classification or segmentation, which requires supervised learning,

not recommender system.

Option C is incorrect because recurrent neural networks (RNN) are not the most efficient approach to

build a model that identifies defects in products based on images of the product taken at the end of

the assembly line. RNNs are a type of neural networks that can process sequential data, such as text,

speech, or video, by maintaining a hidden state that captures the temporal dependencies3. RNNs are

more suitable for problems that involve natural language processing, speech recognition, or video

analysis3. However, defect detection is a problem that involves image classification or segmentation,

which does not require temporal dependencies, but rather spatial dependencies. Moreover, RNNs

are computationally expensive and prone to vanishing or exploding gradients4.

Option D is correct because convolutional neural networks (CNN) are the best approach to build a

model that identifies defects in products based on images of the product taken at the end of the

assembly line. CNNs are a type of neural networks that can process image data, by applying

convolutional filters that extract local features and reduce the dimensionality of the data5. CNNs are

more suitable for problems that involve image classification, object detection, or

segmentation5. CNNs can preprocess the images with lower computation to quickly extract features

of defects in products, by using techniques such as pooling, dropout, or batch normalization6.

Reference:

Reinforcement learning

Recommender system

Recurrent neural network

Vanishing and exploding gradients

Convolutional neural network

CNN techniques

[Defect detection]

[Image classification]

[Image segmentation]