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

Kurssisisältö

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

Tehtävä

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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.

Ratkaisu

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Kiitos palautteestasi!

Osio 3. Luku 7
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book
Challenge: Optimising Function Of Multiple Variables

Tehtävä

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.

Ratkaisu

Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Oliko kaikki selvää?

Miten voimme parantaa sitä?

Kiitos palautteestasi!

Osio 3. Luku 7
Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Pahoittelemme, että jotain meni pieleen. Mitä tapahtui?
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