In the previous series, we uncovered how data is represented, transformed and structured through the geometry of Linear Algebra. Now in our quest to understand the mathematical architecture of machine learning, we hopped on to a new destination where the objective is to uncover the precise mechanics …

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 …

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 …