DQN
This post assumes that you have a strong understanding of the basics of Reinforcement Learning, MDP, DQN and Policy Gradient Algorithms. You can go through Policy Gradients to understand the derivation for Stochastic Policies In the previous post on Actor Critic, we saw the advantage of merging Value based and Policy based methods together. The […]
#CellStratAILab #disrupt4.0 #WeCreateAISuperstars #AlwaysUpskilling Reinforcement Learning (RL) refers to training agents with help of incentive-driven environments. RL typically involves a tuple of <state, action, reward> paradigm, which means that the agent has action choices to make in various states, and each action entails a potential reward. This also means that each state has a “value” […]
I conducted an Introductory session on Reinforcement Learning Policy Gradients (PG) at CellStrat AI Lab on 1st Feb 2020. The goal of this session was to explain the basic underlying principle of Policy Gradients. The session started off with a quick recap of Reinforcement Learning, so that the audience is well aware of the definitions […]
#CellStratAILab #disrupt4.0 #WeCreateAISuperstars Last Saturday saw some amazing sessions on advanced AI at the CellStrat AI Lab meetup. Diabetes prediction with Machine Learning :- First came a superb presentation by Dr Purnendu Das on diabetes prediction using ML. Dr Das started by discussing the data sources for healthcare and clinical data analysis. He covered the […]
#CellStratAILab #disrupt4.0 #WeCreateAISuperstars We had another round of deep AI sessions last Saturday in BLR AI Lab meetup. I started with a detailed deep-dive on the Maximum Likelihood Estimation (MLE) algorithm for Logistic Regression, which is a type of classification technique. Logistic Regression has a concave optimization curve wherein we try to maximize the Likelihood […]