In the first blog of this series we tried to connect the dot between Machine Learning ML and Calculus. We ended up with concepts such as learning objective, cost function as a measure of determining whether we have reached the objective and iteratively we calculate the loss as a metric of …

Gradient Descent gives us the opportunity to build any intelligent system where the output can be optimized by calculating the loss with respect to the system parameters and updating the parameters in the direction that minimized the loss. Chain Rule on the other hand, another mechanism that helps …