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Lära Challenge: Optimising Function Of Multiple Variables | Mathematical Analysis
Mathematics for Data Analysis and Modeling
course content

Kursinnehåll

Mathematics for Data Analysis and Modeling

Mathematics for Data Analysis and Modeling

1. Basic Mathematical Concepts and Definitions
2. Linear Algebra
3. Mathematical Analysis

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Challenge: Optimising Function Of Multiple Variables

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The most commonly used loss function in linear regression is the Mean Squared Error (MSE) loss function. This function is the squared Euclidean distance between the variable's real value and the value we obtained using linear regression approximation. Since this is a function of several variables, we can optimize it using gradient descent.

Your task is to use the optimization method to find the best parameters of the linear regression function:

  1. Create an initial_params variable that will store initial values for parameters of the linear regression function.
  2. Provide minimization of the MSE function.
  3. Get the resulting optimal values of parameters.

Note

You can find more information about linear regression in Linear Regression with Python course.

Lösning

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 3. Kapitel 7
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book
Challenge: Optimising Function Of Multiple Variables

Uppgift

Swipe to start coding

The most commonly used loss function in linear regression is the Mean Squared Error (MSE) loss function. This function is the squared Euclidean distance between the variable's real value and the value we obtained using linear regression approximation. Since this is a function of several variables, we can optimize it using gradient descent.

Your task is to use the optimization method to find the best parameters of the linear regression function:

  1. Create an initial_params variable that will store initial values for parameters of the linear regression function.
  2. Provide minimization of the MSE function.
  3. Get the resulting optimal values of parameters.

Note

You can find more information about linear regression in Linear Regression with Python course.

Lösning

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 3. Kapitel 7
Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
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