For an AI algorithm the learning process is an iterative way of maximizing its understanding of the input and minimizing the gap between algorithm’s output and actual values. It appears to be riding on top of loss function and taking a gradual descent in each iteration. But how does the …

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 …

When we talked about Gradient Descent in part 3, we found it out to be an extremely meaningful optimization technique. Though gradient descent and its variations like mini batch and stochastic gradient descent act as dependable building blocks of AI optimization, they do have some limitations. One …