Learn how to use unsupervised learning to group unlabeled photos into different sets based on their patterns and features, using techniques such as K-means and deep learning.
Table of Contents
Question
You want to use machine learning to discover the underlying pattern and group a collection of unlabeled photos into different sets. Which should you use?
A. Large language models (LLM)
B. Unsupervised learning
C. Supervised learning
D. Bard
Answer
B. Unsupervised learning
Explanation
The correct answer is B. Unsupervised learning. This is a good explanation because unsupervised learning is a type of machine learning that does not require labels or targets, but rather groups data points based on their similarity or structure.
Some of the other options are incorrect or incomplete because:
- A. Large language models (LLM) are not a type of machine learning, but rather a specific kind of neural network that can generate natural language from data.
- C. Supervised learning is a type of machine learning that requires labels or targets, and learns to predict them from the input data.
- D. Bard is not a machine learning term, but rather a word that means a poet or a singer.
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