Practice - Prepare Data for Deep Learning
Practice Questions
Test your understanding with targeted questions
What is the purpose of feature scaling in deep learning?
💡 Hint: Think about how feature magnitudes impact training.
Explain one-hot encoding in your own words.
💡 Hint: Consider how many unique categories you typically have.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What does feature scaling aim to achieve in deep learning?
💡 Hint: Consider how features of different magnitudes affect the training.
True or False: One-hot encoding is not needed if you are using sparse_categorical_crossentropy.
💡 Hint: Compare it against regular categorical_crossentropy use.
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Challenge Problems
Push your limits with advanced challenges
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.
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.
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Reference links
Supplementary resources to enhance your learning experience.