https — //www.youtube.com/watch?v=qg-_EVPqizM&t=3545s — 10-topic bundle

When building complex UIs, standard components can become rigid or difficult to reuse. Advanced patterns give you more control and flexibility.

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Reinforcement Learning (RL) involves an agent learning to make decisions in an environment to maximize a cumulative reward. Q-Learning is a popular, model-free (meaning it doesn't need to know how the environment works beforehand) RL algorithm.

At its heart, Q-Learning builds a Q-table. This table stores a "Q-value" for every possible state and action pair. A Q-value represents the expected future reward if you take a specific action in a specific state and then continue to act optimally.

Probability is all about how likely an event is to happen. We usually express it as a fraction, decimal, or percentage.

The basic formula for the probability of an event (P(A)) is:

$P(A) = \text{(Number of favorable outcomes)} / \text{(Total number of possible outcomes)}$

To begin your programming journey, you need to set up your computer with the right tools. This involves installing a programming language, a code editor, and a version control system.

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Probability theory is built on a few key ideas:

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When we talk about probability, we're dealing with experiments whose outcomes aren't fixed. To properly analyze these, we need to clearly define what can happen.

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Probability is a numerical measure of the likelihood that an event will occur. It's always a value between 0 and 1 (or 0% and 100%).

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When dealing with more than one event, you often need to combine their probabilities. The two fundamental rules for doing this are the Addition Rule and the Multiplication Rule.

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