Month: December 2020

Mathematics for Machine Learning Resources

Mathematics for Machine Learning Resources

Every day, computational fields like machine learning, artificial intelligence, neuroscience, cryptography, etc. are making great progress. Anyone pursuing a career in all such fields might be looking for a solid foundation. If we dig deeper, we often trace back to the Queen of Science- Mathematics. A solid foundation of the math concepts will be useful …

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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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art of thinking clearly

Book summary: The Art Of Thinking Clearly by Rolf Dobelli

Hello, world, In this post, I summarize the book The Art Of Thinking Clearly by Rolf Dobelli. This book has some interesting observations about thinking clearly. I have listed out the main ideas in bold, its summary from respective chapters, along with my thoughts and view points. Introduction to Thinking Clearly Failure to think clearly …

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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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