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9 questions
What is the primary difference between supervised and unsupervised learning in machine learning?
Which of the following best describes a loss function in machine learning?
Which regularization technique adds a penalty proportional to the absolute value of coefficients to prevent overfitting?
Neural networks are inspired by the structure and function of the human brain.
Gradient descent can be used to find the optimal weights in a neural network by minimizing the loss function.
Support Vector Machines (SVMs) are typically used only for regression tasks.
Explain in brief what a decision tree algorithm does in machine learning.
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Describe how regularization helps improve a machine learning model.
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What role does the learning rate play in the gradient descent algorithm?
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