The algorithm known for finding optimal solutions by iteratively minimizing a cost function is called Gradient Descent.

Here are the steps it follows:

1. Initialize: Start with random values for the parameters (weights).

2. Compute: Calculate the cost function, which measures the error of the prediction of the model.

3. Gradient Calculation: Compute the gradient of the cost function. The gradient is a derivative operation that calculates the slope of the function at a specific point.

4. Update Parameters: Adjust the parameters in the direction that minimizes the cost function. This is done by subtracting the gradient of the cost function from the current parameters.

5. Iterate: Repeat steps 2-4 until the cost function is minimized to a satisfactory level or after a certain number of iterations.

6. Terminate: The algorithm stops when the cost function is at its minimum. The parameters at this point are considered to be the optimal parameters.

Question

The algorithm known for finding optimal solutions by iteratively minimizing a cost function is called Gradient Descent.

Here are the steps it follows:

1. Initialize: Start with random values for the parameters (weights).

2. Compute: Calculate the cost function, which measures the error of the prediction of the model.

3. Gradient Calculation: Compute the gradient of the cost function. The gradient is a derivative operation that calculates the slope of the function at a specific point.

4. Update Parameters: Adjust the parameters in the direction that minimizes the cost function. This is done by subtracting the gradient of the cost function from the current parameters.

5. Iterate: Repeat steps 2-4 until the cost function is minimized to a satisfactory level or after a certain number of iterations.

6. Terminate: The algorithm stops when the cost function is at its minimum. The parameters at this point are considered to be the optimal parameters.

Knowee AI · Accepted Answer

The algorithm known for finding optimal solutions by iteratively minimizing a cost function is called Gradient Descent.

Here are the steps it follows:

1. Initialize: Start with random values for the parameters (weights).

2. Compute: Calculate the cost function, which measures the error of the prediction of the model.

3. Gradient Calculation: Compute the gradient of the cost function. The gradient is a derivative operation that calculates the slope of the function at a specific point.

4. Update Parameters: Adjust the parameters in the direction that minimizes the cost function. This is done by subtracting the gradient of the cost function from the current parameters.

5. Iterate: Repeat steps 2-4 until the cost function is minimized to a satisfactory level or after a certain number of iterations.

6. Terminate: The algorithm stops when the cost function is at its minimum. The parameters at this point are considered to be the optimal parameters.

Which algorithm is known for finding optimal solutions by iteratively minimizing a cost function?

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