Course Content

Introduction to TensorFlow

1. Tensors

Welcome to TensorFlow Introduction to Tensors Tensor Properties Applications of Tensors Batches Creating Tensors Data Types Basic Operations: Arithmetic Basic Operations: Linear Algebra Creating a Neural Network Layer Transformations Reduction Operations Quiz

2. Basics of TensorFlow

Gradient Tape Graph Execution Neural Network Implementation Final Quiz Summary

Quiz

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?

Select the correct answer

(3,)

(10, 2)

(1, 2, 3, 4)

(1, 1, 1)

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

Select the correct answer

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

Select the correct answer

Tensor of strings.

Tensor of integer numbers.

Tensor of boolean values.

Tensor of non-negative integer numbers.

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

Select the correct answer

The number of columns in the first matrix must equal the number of rows in the second matrix.

The number of rows in the first matrix must equal the number of columns in the second matrix.

The two matrices must be square matrices.

The two matrices must be of the same shape.

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

Select the correct answer

tf.multiply

tf.tensordot with axes=1 parameter.

tf.add

tf.reduce_mean with axes=0 parameter.

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

Select the correct answer

All rows from the third column.

All columns from the third row.

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

Select the correct answer

tf.multiply

tf.reshape

tf.concat

tf.divide

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

Select the correct answer

TensorFlow will consider the last axis (with the biggest index).

TensorFlow will throw an error.

TensorFlow will reduce across all axes and produce a scalar.

TensorFlow will produce a tensor of the same shape.

Which command calculates the mean value of a tensor?

Select the correct answer

tf.reduce_max

tf.reduce_min

tf.reduce_sum

tf.reduce_mean

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?

Select the correct answer

(300, 50)

(10, 50, 30, 24)

(30, 50, 10)

(15000,)

Everything was clear?

Section 1. Chapter 13

Course Content

Introduction to TensorFlow

1. Tensors

2. Basics of TensorFlow

Gradient Tape Graph Execution Neural Network Implementation Final Quiz Summary

Introduction to TensorFlow

Quiz

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?

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

Select the correct answer

(3,)

(10, 2)

(1, 2, 3, 4)

(1, 1, 1)

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

Select the correct answer

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

Select the correct answer

Tensor of strings.

Tensor of integer numbers.

Tensor of boolean values.

Tensor of non-negative integer numbers.

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

Select the correct answer

The number of columns in the first matrix must equal the number of rows in the second matrix.

The number of rows in the first matrix must equal the number of columns in the second matrix.

The two matrices must be square matrices.

The two matrices must be of the same shape.

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

Select the correct answer

tf.multiply

tf.tensordot with axes=1 parameter.

tf.add

tf.reduce_mean with axes=0 parameter.

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

Select the correct answer

All rows from the third column.

All columns from the third row.

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

Select the correct answer

tf.multiply

tf.reshape

tf.concat

tf.divide

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

Select the correct answer

TensorFlow will consider the last axis (with the biggest index).

TensorFlow will throw an error.

TensorFlow will reduce across all axes and produce a scalar.

TensorFlow will produce a tensor of the same shape.

Which command calculates the mean value of a tensor?

Select the correct answer

tf.reduce_max

tf.reduce_min

tf.reduce_sum

tf.reduce_mean

Select the correct answer

(300, 50)

(10, 50, 30, 24)

(30, 50, 10)

(15000,)

Everything was clear?

Section 1. Chapter 13