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

Course Content

Mathematics for Data Analysis and Modeling

Mathematics for Data Analysis and Modeling

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

Challenge: Optimising Function Of Multiple Variables

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

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

Everything was clear?

Section 3. Chapter 7

Challenge: Optimising Function Of Multiple Variables

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

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

Everything was clear?

Section 3. Chapter 7

Challenge: Optimising Function Of Multiple Variables

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

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

Everything was clear?

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