ML Fundamentals
Reinforcement Learning (RL)
An AI that learns by trial and error, getting rewards for good actions and penalties for bad ones, like training a dog.
Definition
A learning paradigm where an agent learns optimal behavior by interacting with an environment and receiving feedback (rewards or penalties). The agent learns a policy that maximizes cumulative reward.
Why it matters
Powers game-playing AI (AlphaGo), robotics, and recommendation systems that learn from user behavior.
Related terms in ML Fundamentals
Activation Functions
The "switch" inside a neural network that decides whether a neuron should fire, allowing the AI to learn complex non-linear patterns.
Active Learning
A technique where the AI asks humans to label only the most confusing examples, saving time and money on data labeling.
Anomaly Detection
Finding the "weird" stuff in a dataset, like a credit card charge in a foreign country or a broken machine part.
Artificial General Intelligence (AGI)
A hypothetical "super-AI" that can learn and do any intellectual task a human can do, not just one specific thing.
From vocabulary to outcomes
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