Practice Understanding Numpy For Machine Learning (3) - Chapter 3: Understanding NumPy for Machine Learning

Practice: Understanding NumPy for Machine Learning

Lesson 3Understanding NumPy for Machine Learning

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10 questions

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4 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
4 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is the command to create a 2D NumPy array with values [[1, 2], [3, 4]]?
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Think about the syntax `np.array()`.
Question 2.
What does np.zeros((3, 3)) do?
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Recall what zeros represent in initialization.
3Question 3.
What does NumPy stand for?
- Numerical Python
- Normal Python
- Numpy Language
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Think about the focus of this library.
4Question 4.
True or False: NumPy arrays are slower than Python lists in numerical operations.
- True
- False
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Think about why we use NumPy in machine learning.
Question 5.
You are given an array of shape (3, 4). Reshape it into a shape of (2, 6). How do you do it, and what does the resulting array look like?
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Remember, the total number of elements must stay constant.
Question 6.
Implement a Python function using NumPy to normalise an array (subtract the mean and divide by the standard deviation). What will be the general structure of your function?
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Think about mean and std calculations, and how they relate to normalizing data.

6 more questions in this section. Enroll free to practise them all.