Enter your learning rate, initial guess, and number of iterations into the calculator to determine your calculated θ value.

## Gradient Descent Calculation Formula

The following formula is used to calculate the new value of θ in each iteration of gradient descent.

θ = θ - α * ∇J(θ)

Variables:

- θ is the parameter we are optimizing
- α is the learning rate
- ∇J(θ) is the gradient of the cost function with respect to θ

To calculate the new value of θ, subtract the product of the learning rate and the gradient of the cost function from the current value of θ. Repeat this process for the specified number of iterations.

## What is Gradient Descent?

Gradient descent is an optimization algorithm used to minimize the cost function in machine learning models. By iteratively updating the parameters of the model in the direction of the negative gradient of the cost function, gradient descent seeks to find the