Learn the correct data and label formats used when training a machine learning model to recognize and classify images of handwritten digits 0 through 9. Understand how images are used as input data while one-hot encoding represents the digit labels.
Table of Contents
Question
Identify the TRUE statements with regards to training a model to recognize handwritten digits.
A. One-hot-encoded representation of the numbers 0 through 9 are provided as data
B. Images of handwritten digits 0 through 9 are provided as data
C. One-hot-encoded representation of the numbers 0 through 9 are provided as labels
D. Images of handwritten digits 0 through 9 are provided as labels
Answer
B. Images of handwritten digits 0 through 9 are provided as data
C. One-hot-encoded representation of the numbers 0 through 9 are provided as labels
Explanation
When training a model to recognize handwritten digits, the following statements are TRUE:
B. Images of handwritten digits 0 through 9 are provided as data
The input data used to train the model consists of actual images, specifically handwritten versions of the digits 0 through 9. The model learns to recognize the visual patterns and characteristics of each handwritten digit based on a large dataset of labeled example images.
C. One-hot-encoded representation of the numbers 0 through 9 are provided as labels
While the images of the digits are the input data, the labels that indicate the correct classification for each image are provided in a one-hot-encoded format. One-hot encoding uses a vector where each possible digit value is represented by a separate element. The element corresponding to the correct digit class is set to 1 while all other elements are 0. For example, the one-hot-encoded label for digit 3 would be [0, 0, 0, 1, 0, 0, 0, 0, 0, 0].
So in summary, the model is trained on many example images of handwritten digits as its data, and learns to predict the corresponding one-hot-encoded label vector that identifies the correct digit 0-9 for each image. The combination of image data and one-hot-encoded labels allows the model to learn the mapping between the visual representation of each digit and its abstract class.
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