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AI workshop - Day 2

Authored by Sarah Farag

Computers, Arts

University

Used 17+ times

AI workshop - Day 2
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18 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Traditional machine learning and deep learning are the core technologies of artificial

intelligence. There is a slight difference in the engineering process. The following steps. What

you don't need to do in deep learning is:

Model evaluation

Feature engineering

Data cleaning

Model building

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which of the following description of the validation set is wrong?

The verification set can coincide with the test set.

The test set can coincide with the training set

The subset used to pick hyperparameters is called a validation set.

Typically 80% of the training data 1s used for training and 20% 1s used for verification.

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What are the common clustering algorithms?

Density clustering

Hierarchical clustering

Spectral clustering

k-means

All the above

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

The model composed of machine learning algorithms cannot represent the true data

distribution function on a theoretical level. Just approach it.

TRUE

FALSE

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the performance of artificial intelligence in the stage of perceptual intelligence?

Machines begin to understand, think and make decisions like humans.

Machines begin to calculate and transmit information just like humans.

The machine starts to understand and understand, make judgments, and take some simple actions

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which of the following is true about unsupervised learning?

Unsupervised algorithm only processes "features" and does tags.

Dimensionality reduction algorithm is not unsupervised learning

K-means algorithm and SVM algorithm belong lo unsupervised learning.

none of the above.

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

The training error will continue to decrease as the model complexity increases.

TRUE

FALSE

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