Explain learning paradigms, loss, and embeddings

Q: Explain learning paradigms, loss, and embeddings

This question evaluates understanding of core machine learning concepts—learning paradigms (supervised, unsupervised, self-supervised), loss functions, and embeddings—and the competency to explain their roles and interrelations in model training and inference.

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Question

ML fundamentals (oral)

Answer the following conceptual questions clearly and with examples:

What is supervised learning? What are typical inputs/labels and tasks?
What is unsupervised learning? What problems does it solve?
What is self-supervised learning? How is it different from supervised/unsupervised?
What is a loss function? Why do we need it, and what are a few common examples?
What is an embedding?
- What does it represent?
- How is it learned?
- How is it used at inference time?
Give several real-world use cases for embeddings , especially in personalization or search.

Explain learning paradigms, loss, and embeddings

Quick Overview

ML fundamentals (oral)

Solution

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