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Quiz: TensorFlow Basics | Tensors
Introduction to TensorFlow
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Зміст курсу

Introduction to TensorFlow

Introduction to TensorFlow

1. Tensors
2. Basics of TensorFlow

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Quiz: TensorFlow Basics

1. Given a tensor of rank `3`, what its shape can look like?
2. For a tensor of shape `(1, 4)`, can you identify its rank?
3. Which of the following tensors corresponds to the data type `uint32`?
4. Which of the following is true about the matrix multiplication (dot product) compatibility rule?
5. Which of the following is an alternative to matrix multiplication using `tf.matmul`?
6. What does the slice `X[:, 2]` represent for a matrix?
7. Which of the following operations can be used to combine multiple tensors?
8. In a reduction operation, if we don't specify an axis:
9. Which command calculates the mean value of a tensor?
10. A website tracks user activities for `50` users over a month (`30` days). Every day, it logs `10` different metrics (like time spent on site, clicks, purchases, etc.) for each user (`15000` values in total). What tensor shape encapsulates this data?
Given a tensor of rank `3`, what its shape can look like?

Given a tensor of rank 3, what its shape can look like?

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For a tensor of shape `(1, 4)`, can you identify its rank?

For a tensor of shape (1, 4), can you identify its rank?

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Which of the following tensors corresponds to the data type `uint32`?

Which of the following tensors corresponds to the data type uint32?

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Which of the following is true about the matrix multiplication (dot product) compatibility rule?

Which of the following is true about the matrix multiplication (dot product) compatibility rule?

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Which of the following is an alternative to matrix multiplication using `tf.matmul`?

Which of the following is an alternative to matrix multiplication using tf.matmul?

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What does the slice `X[:, 2]` represent for a matrix?

What does the slice X[:, 2] represent for a matrix?

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Which of the following operations can be used to combine multiple tensors?

Which of the following operations can be used to combine multiple tensors?

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In a reduction operation, if we don't specify an axis:

In a reduction operation, if we don't specify an axis:

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Which command calculates the mean value of a tensor?

Which command calculates the mean value of a tensor?

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A website tracks user activities for `50` users over a month (`30` days). Every day, it logs `10` different metrics (like time spent on site, clicks, purchases, etc.) for each user (`15000` values in total). What tensor shape encapsulates this data?

A website tracks user activities for 50 users over a month (30 days). Every day, it logs 10 different metrics (like time spent on site, clicks, purchases, etc.) for each user (15000 values in total). What tensor shape encapsulates this data?

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