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Probability Intuition for Language Models

Learn probabilities, expectation, and conditional probability $P(A|B)$ which dictates next-token generation.

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🎓 Learning objectives

  • •Understand basic probability values (0.0 to 1.0)
  • •Explain conditional probability and Bayes' Theorem
  • •Calculate expected value from a set of probabilities

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📂 Subtopics

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Probability Fundamentals: Events, Distributions and Joint/Conditional/Marginal Probability

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Bayes' Theorem: Prior, Likelihood and Posterior

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Information Theory Basics: Entropy, Cross-Entropy and KL Divergence

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Probability in LLMs: Next-Token Prediction, Softmax and Perplexity

Related concepts

python-basicsllm-foundations

Next to learn

neural-networks →deep-learning →
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