Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
Data visualization
B
Feature scaling
C
Gradient descent
D
Data collection
Understanding the Answer
Let's break down why this is correct
Answer
Matrix multiplication is the building block that lets linear‑algebra systems combine vectors and matrices to solve equations. In machine‑learning training, the analogous building block is back‑propagation, which propagates error gradients through a network so that the optimizer can adjust weights. Both operations are simple, repeatable steps that enable larger, more complex calculations. For example, a neural network multiplies input vectors by weight matrices, then back‑propagates the loss to update those weights. Thus, back‑propagation plays the same foundational role for ML optimization that matrix multiplication does for linear algebra.
Detailed Explanation
Gradient descent is a method that moves model weights step by step toward the lowest point of a function. Other options are incorrect because Visualizing data helps us see patterns, but it does not change model weights; Feature scaling changes the size of data values, but it does not find the lowest point of a function.
Key Concepts
Matrix Operations
Optimization in Machine Learning
Gradient Descent
Topic
Linear Algebra in Machine Learning
Difficulty
hard level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1In machine learning, how does matrix multiplication facilitate the computation of predictions in a linear regression model?
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2
Question 2In the context of Machine Learning, how does matrix multiplication facilitate the process of gradient descent when optimizing a model's parameters?
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3
Question 3In machine learning, the performance of a model often improves with the optimization of its parameters through linear algebra techniques. What is the underlying reason why matrix operations are so critical in this optimization process?
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