I’d like to demonstrate the kernel trick using a simple example here. The equations are taken from [1]. My motivation is to look from a more practical perspective. Inputs and Outputs Of course we have X as input and y as output. But I want to talk about inputs to the prediction equation to solve…

This is pretty explanatory. Here are some remarks on the meanings: Predictions specify whether the output is positive or negative Match between prediction and actual condition specify if the output is true or false Precision Precision is the measure of how we are successfully classifying the positives. More clearly, how much of the positive outputs…

We start with a basic formula, expected value. It is the expected value when you follow some transition function with some start state . Eq. 1: Note that the initial state is included in the reward function . Bellman TheoremA policy is optimal if and only if it is greedy with respect to its induced…

It is a mapping of words to the vector space. Words will be represented as vectors to be used in machine learning domain. We can utilize this transformation and treat the words as mathematical objects to extract the required information. Granted we have the embeddings, we can do operations easily. Now it comes how to…

Definitions 1. Pure Nash Equilibrium It is when there is a state in which no player can make better profit by changing their action guaranteed no one else will change unreasonably their choice (other players are logical players) 2. Mixed Nash Equilibrium It is when you don’t know the opponents movement, or it is a…