where ηη is a small, positive parameter (known as the learning rate). Then Equation (9) tells us that ΔC≈−η∇C⋅∇C=−η∥∇C∥2ΔC≈−η∇C⋅∇C=−η‖∇C‖2. Because ∥∇C∥2≥0‖∇C‖2≥0, this guarantees that ΔC≤0ΔC≤0, i.e., CC will always decrease, never increase, if we change vv according to the prescription in (10). (Within, of course, the limits of the approximation in Equation (9)). This is exactly the property we wanted! And so we'll take Equation (10) to define the "law of motion" for the ball in our gradient descent algorithm. That is, we'll use Equation (10) to compute a value for ΔvΔv, then move the ball's position vv by that amount: