Probability
Markov Chains: The Building Block to Key Stochastic Processes. You Need To Know This!!
An introduction with code examples of the key stochastic process behind reinforement learning, stock market price movements, and atomic movement
Probability Fundamentals You Need To Know: Getting Up To Speed In One Article!
What do you do when you have forgotten the fundamentals of probability or when you have never had a formal education? In this article, I hope to bring you up to speed with the basic foundations required to study intermediate and advanced parts of probability theory. I hope that this will also serve as a useful reference article for those who are rusty or had a spotty education in the topic.
Multiple Random Variables: Marginal, Joint, and Conditional Distributions
An introduction to the fundamental mathematical objects in the analysis of multiple random variables with Python examples
Reinforcement Learning
Markov Decision Processes - The Framework For Decision-Making Under Uncertainty
Markov Decision Processes are the essential process which we use in dynamic programming and thus reinforcement learning problems. It is an extension of Markov chains and provides the theoretical backbone for training an AI agent under iterative environments and rewards based off of decisions.
K-Armed Bandits: The Canonical RL Problem
The main toy problem behind reinforcement learning
What is Reinforcement Learning?
Key RL concepts and history
