Conceptual

An Intuitive Explanation of Naive Bayes Classifier

Introduction In this post, let’s take a look at the intuition behind Naive Bayes Classifier used in machine learning. Naive Bayes classifier is one of the basic algorithms often encountered in machine learning applications. If linear regression was based on concepts from linear algebra and calculus, naive Bayes classifier mostly backed up by probability theory. …

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distribution

Nine key papers in Distributional Reinforcement Learning Literature

In this post, I am going to give a summary of nine key papers from the distributional reinforcement learning (DRL) area. Paper 001 : A Distributional Perspective on Reinforcement Learning  This is the seminal paper in this area. The key idea of  the paper is the argument that the value distribution is important in reinforcement …

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reinforcement learning framework

Paper Note: Distributional Reinforcement Learning with Quantile Regression

This article is a tutorial style discussion of the research paper titled “Distributional RL with Quantile Regression”. Here I try to reflect and convey the ideas of the paper with an intuitive style. Introduction Supervised machine learning deals with learning from examples. This is in contrast to real-life where we often don’t have a proper …

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distributional reinforcement learning

Summary of research paper “A Distributional Perspective on Reinforcement Learning”

Overview In this note about distributional reinforcement learning, I am going to reflect on the paper titled A Distributional Perspective on Reinforcement Learning. I will try to give an overview of the underlying ideas behind this paper. Please keep in mind that a research contribution is often a culmination of multiple ideas/components. This makes from …

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