Practice Self-supervised Learning (11.2.3) - Representation Learning & Structured Prediction
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Self-Supervised Learning

Practice - Self-Supervised Learning

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is self-supervised learning?

💡 Hint: Think about a way to learn from data without needing labels.

Question 2 Easy

What are masked prediction models?

💡 Hint: Consider how BERT processes sentences.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the main goal of self-supervised learning?

To learn from labeled data
To learn from structured data
To learn from unlabeled data

💡 Hint: Think about the limitations of requiring labeled data.

Question 2

True or False: In contrastive learning, the aim is to maximize the similarity between dissimilar pairs.

True
False

💡 Hint: Consider the definitions of similarity and dissimilarity.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Explain how contrastive learning could be used to differentiate between two types of fruit images. What features would the model focus on?

💡 Hint: Think about what makes an apple different from an orange.

Challenge 2 Hard

Design a small dataset for training a masked prediction model in text. What kinds of sentences would be suitable, and what techniques would you employ for masking?

💡 Hint: Consider generating sentences with meaningful context for the model to predict.

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Reference links

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