The Mathematics and Geometry of Gradient Descent

Gradient Descent is one of the most widely used Optimization techniques that has profound use in Machine learning. Although one of the more simpler methods because of its ease of use and less memory requirements it has been widely adopted in the Data Science and Machine Learning Community. If we have a cost function where […]

Process Performance Maximization through Input Parameters Optimization using Taylor Series Expectation Approximation

Process Performance Maximization through Input Parameters Optimization using Taylor Series Expectation Approximation
Abstract In several industrial settings or manufacturing units the process output is expressed as a function of several inputs and such relationships can be expressed in the form of the equation based on the prior data or known truth. It is desirable in such a setting that the output is within a specified threshold for […]

A start to Bayesian Inference

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Frequentist looks at the maximum likelihood and chose the model that maximizes the probability of the seen Data given the model i,e P(Data/model) . Doesn’t care of what has happened in the past or what constraints the system should be restricted to.Works well when the current data is true representation of the domain. Bayesians are more […]