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Question 187 - MLS-C01 discussion

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The chief editor for a product catalog wants the research and development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company's retail brand. The team has a set of training data.

Which machine learning algorithm should the researchers use that BEST meets their requirements?

A.
Latent Dirichlet Allocation (LDA)
Answers
A.
Latent Dirichlet Allocation (LDA)
B.
Recurrent neural network (RNN)
Answers
B.
Recurrent neural network (RNN)
C.
K-means
Answers
C.
K-means
D.
Convolutional neural network (CNN)
Answers
D.
Convolutional neural network (CNN)
Suggested answer: D

Explanation:

The problem of detecting whether or not individuals in a collection of images are wearing the company's retail brand is an example of image recognition, which is a type of machine learning task that identifies and classifies objects in an image. Convolutional neural networks (CNNs) are a type of machine learning algorithm that are well-suited for image recognition, as they can learn to extract features from images and handle variations in size, shape, color, and orientation of the objects. CNNs consist of multiple layers that perform convolution, pooling, and activation operations on the input images, resulting in a high-level representation that can be used for classification or detection. Therefore, option D is the best choice for the machine learning algorithm that meets the requirements of the chief editor.

Option A is incorrect because latent Dirichlet allocation (LDA) is a type of machine learning algorithm that is used for topic modeling, which is a task that discovers the hidden themes or topics in a collection of text documents. LDA is not suitable for image recognition, as it does not preserve the spatial information of the pixels. Option B is incorrect because recurrent neural networks (RNNs) are a type of machine learning algorithm that are used for sequential data, such as text, speech, or time series. RNNs can learn from the temporal dependencies and patterns in the input data, and generate outputs that depend on the previous states. RNNs are not suitable for image recognition, as they do not capture the spatial dependencies and patterns in the input images. Option C is incorrect because k-means is a type of machine learning algorithm that is used for clustering, which is a task that groups similar data points together based on their features. K-means is not suitable for image recognition, as it does not perform classification or detection of the objects in the images.

References:

Image Recognition Software - ML Image & Video Analysis - Amazon ...

Image classification and object detection using Amazon Rekognition ...

AWS Amazon Rekognition - Deep Learning Face and Image Recognition ...

GitHub - awslabs/aws-ai-solution-kit: Machine Learning APIs for common ...

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asked 16/09/2024
Arkadius Thoma
49 questions
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