Modern NLP: Classical Language Models

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Related lectures (29)

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Language Models: Fixed-context and Recurrent Neural Networks

Discusses language models, focusing on fixed-context neural models and recurrent neural networks.

State Space Models: Expressivity of Transformers

Covers state space models and the expressivity of transformers in sequence copying tasks.

Probability and Statistics

Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.

Conditional Density and Expectation

Explores conditional density, expectations, and independence of random variables with practical examples.

Elements of Statistics: Probability and Random Variables

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Probability and Statistics

Explores joint random variables, conditional density, and independence in probability and statistics.

Continuous Random Variables

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Lexicons, n-grams and Language Models

Explores lexicons, n-grams, and language models, emphasizing their importance in recognizing words and the effectiveness of n-grams for various tasks.

Neuromorphic Computing: Concepts and Hardware Implementations

Covers neuromorphic computing, challenges in ternary and binary computing, hardware simulations of the brain, and new materials for artificial brain cells.

Conditional Probability: Prediction Decomposition

Explores conditional probability, Bayes' theorem, and prediction decomposition for informed decision-making.