Practice Prepare Data for Deep Learning - lab.1 | Module 6: Introduction to Deep Learning (Weeks 11) | Machine Learning
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lab.1 - Prepare Data for Deep Learning

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the purpose of feature scaling in deep learning?

💡 Hint: Think about how feature magnitudes impact training.

Question 2

Easy

Explain one-hot encoding in your own words.

💡 Hint: Consider how many unique categories you typically have.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What does feature scaling aim to achieve in deep learning?

  • Equal contribution of all features
  • Faster computation
  • Higher accuracy

💡 Hint: Consider how features of different magnitudes affect the training.

Question 2

True or False: One-hot encoding is not needed if you are using sparse_categorical_crossentropy.

  • True
  • False

💡 Hint: Compare it against regular categorical_crossentropy use.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You have a dataset consisting of several features with different ranges (e.g., age in years, salary in dollars). Explain how you would preprocess this dataset before feeding it into a neural network, outlining the specific techniques used.

💡 Hint: Focus on each feature's impact on the training process.

Question 2

Consider a classification problem where you need to categorize articles into topics. You have integer labels (0 for politics, 1 for sports, etc.). Discuss what issues might arise from using these integer labels directly, and how would you remedy them using one-hot encoding.

💡 Hint: Think about the model's perspective and how it interprets input data.

Challenge and get performance evaluation