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We have a function to find the gradient of b at every point. To find the m gradient, or the way the loss changes as the slope of our line changes, we can use this formula:

2N∑i=1N−xi(yi−(mxi+b))\frac{2}{N}\sum_{i=1}^{N}-x_i(y_i-(mx_i+b))

Once more:

  • N is the number of points you have in your dataset
  • m is the current gradient guess
  • b is the current intercept guess

To find the m gradient:

  • we find the sum of x_value * (y_value - (m*x_value + b)) for all the y_values and x_values we have
  • and then we multiply the sum by a factor of -2/N. N is the number of points we have.

Once we have a way to calculate both the m gradient and the b gradient, we’ll be able to follow both of those gradients downwards to the point of lowest loss for both the m value and the b value. Then, we’ll have the best m and the best b to fit our data!

Instructions

1.

Define a function called get_gradient_at_m() that takes in a set of x values, x, a set of y values, y, a slope m, and an intercept value b.

For now, have it return m.

2.

In this function, we want to go through all of the x values and all of the y values and compute x*(y - (m*x+b)) for each of them.

Create a variable called diff that has the sum of all of these values, and return it from the function.

3.

Define a variable called m_gradient and set it equal to the -2/N multiplied by diff.

Instead of returning diff, return m_gradient.

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