What is the role of test data in evaluating an algorithm?
Deep Learning CNN Convolutional Neural Networks with Python - Sliding Window Implementation

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Information Technology (IT), Architecture
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University
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Hard
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7 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
To improve the algorithm's speed
To train the algorithm
To determine the algorithm's accuracy
To provide examples of incorrect outputs
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is the sliding window technique used in object detection?
To increase the size of training images
To simplify the classification process
To reduce the number of features
To handle large test images
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a key characteristic of the sliding window kernel?
It is always even-sized
It is always odd-sized
It is always square-shaped
It is always rectangular
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does a feature descriptor represent in the sliding window technique?
A small patch of the image
The test data
The entire image
The classifier's output
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How does the classifier use the feature descriptor?
To extract new features
To resize the image
To identify the object in the patch
To train the model
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a limitation of the sliding window technique discussed in the video?
It cannot handle large images
It struggles with objects that are upside down
It only works with grayscale images
It requires a lot of computational power
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What future topic is hinted at in the video regarding object detection?
Increasing training data size
Handling scale and shift invariance
Improving image resolution
Reducing algorithm complexity
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