Following is a list (in progress) of resources on Reinforcement Learning and allied topics. Though not comprehensive, it includes University lectures, YouTube playlists, MOOCs, blogs, etc. Hope this would be useful to someone getting started in reinforcement learning. UC Berkely CS 285 at UC Berkeley Deep Reinforcement Learning (23) Deep RL Bootcamp (15) CS 294: […]
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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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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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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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This article shows two key visual intuitions behind the usage of a discount factor in reinforcement learning with image, code, and video. Introduction Most of the advances in science and technology happened in the last 100 years. We can see mind-boggling progress in automotive, medicine, communication, energy, etc. . Among these advances, some technologies shake
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Have you ever wondered what actually happens between “we have some data” and “there’s an app using AI”? That gap is the machine learning workflow. It is the real journey every ML project takes, and it is far simpler than it sounds. In our last episode, we explored the three ways machines learn: supervised, unsupervised,
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What Is Machine Learning? Understanding the Types of Machine Learning (Supervised, Unsupervised & Reinforcement) Machine Learning sounds intimidating at first. People imagine complex math, endless equations, and years of study before you can understand anything. That reputation, however, is misleading. Once you break Machine Learning into its core ideas, it becomes surprisingly simple. Machine Learning
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This is one among a series of frequently asked interview questions in machine learning published in this website Explain the terms Artificial Intelligence, Machine Learning, Deep Learning, and Reinforcement Learning? Artificial Intelligence (AI) is the domain of intelligent machines which can think rationally and act rationally. It uses algorithms to enable problem solving with robust
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This article is a review of active learning which is a subfield of machine learning Introduction The field of Machine Learning (ML) broadly deals with giving computers the ability to do tasks like humans. We are seeing a human-level performance in specialized tasks such as speech recognition, image recognition, etc. Many sub-fields of ML are
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This is a curated list of resources, blogs, YouTube channels/playlists, and special interest groups which discuss machine learning papers. These machine learning reading groups are an excellent opportunity for anyone working in machine learning to improve their knowledge and skillset and also to network remotely. For any machine learning or data science aspirant or even
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