Unit 5 - Training Convolutional Neural Networks

Unit 5 - Training Convolutional Neural Networks

10 Qs

quiz-placeholder

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Unit 5 - Training Convolutional Neural Networks

Unit 5 - Training Convolutional Neural Networks

Assessment

Quiz

others

Hard

Created by

Arsanchai Sukkuea

Used 4+ times

FREE Resource

10 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the purpose of data normalization when preparing a dataset for training a CNN?
To make the data larger and more complex
To make the data smaller and easier to manage
To scale and center the data to a standard range
To add noise to the dataset

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

In CNN data preparation, what is the role of data augmentation techniques?
To create entirely new synthetic data
To reduce the size of the dataset
To increase the dataset's variability by applying transformations to the existing data
To remove noisy data points

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

When splitting a dataset into training, validation, and test sets for CNN training, what is the typical ratio?
50% training, 25% validation, 25% test
70% training, 20% validation, 10% test
80% training, 10% validation, 10% test
60% training, 30% validation, 10% test

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which type of labeling strategy is used when each data point in a dataset belongs to a single, mutually exclusive category?
Multi-labeling
Binary labeling
Multiclass labeling
Hierarchical labeling

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

When dealing with an imbalanced dataset for CNN training, which of the following techniques can help address this issue?
Removing the majority class data
Oversampling the minority class data
Randomly shuffling the dataset
Ignoring the class imbalance and training as usual

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which file format is commonly used for storing and organizing large image datasets?
.txt
.csv
.jpg
.tfrecord

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the purpose of stratified sampling when splitting a dataset into training and testing subsets?
To ensure each class in the dataset is proportionally represented in both subsets
To increase the dataset size
To randomly shuffle the data
To remove outliers from the dataset

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