A type of machine learning where models learn to make decisions through trial and error.
Reinforcement Learning (RL) is a machine learning paradigm which is focused on training algorithms using a system of rewards and penalties. It imitates the fundamental way that humans and animals learn, making it suitable for a wide range of applications from gaming to autonomous vehicles.
Reinforcement learning algorithms learn optimal actions through trial and error by receiving rewards for successful outcomes and penalties for unsuccessful ones.
Common applications include robotics, self-driving cars, and automated trading systems.
Understand what large language models are, how they power ChatGPT and other AI assistants, and why they are revolutionizing conversational AI.
A training approach where a system learns to map inputs directly to outputs, minimizing intermediate steps or feature engineering.
Discover how AI text generation works, from autocomplete to full conversations. Learn how LLMs predict and generate coherent, contextual text.
A set of rules or procedures for solving a problem or accomplishing a task.
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