📚 Learning Guide
Linear Algebra in Machine Learning
hard

Matrix multiplication in linear algebra is to machine learning optimization as A is to B. What is B?

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Learning Path
Learning Path

Question & Answer
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2
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3
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4
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Choose the Best Answer

A

Data visualization

B

Feature scaling

C

Gradient descent

D

Data collection

Understanding the Answer

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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

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