Lecture
Lexicons, n-grams and Language Models
Related lectures (72)
Words, tokens, n-grams and Language Models
Explores words, tokens, n-grams, and language models, focusing on probabilistic approaches for language identification and spelling error correction.
Words Tokens: Lexical Level Overview
Explores words, tokens, and language models in NLP, covering challenges in defining them, lexicon usage, n-grams, and probability estimation.
Vision-Language-Action Models: Training and Applications
Delves into training and applications of Vision-Language-Action models, emphasizing large language models' role in robotic control and the transfer of web knowledge. Results from experiments and future research directions are highlighted.
Probability and Statistics
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Fundamental Limits of Gradient-Based Learning
Delves into the fundamental limits of gradient-based learning on neural networks, covering topics such as binomial theorem, exponential series, and moment-generating functions.
Quantum Random Number Generation
Explores quantum random number generation, discussing the challenges and implementations of generating good randomness using quantum devices.
Interval Estimation: Method of Moments
Covers the method of moments for estimating parameters and constructing confidence intervals based on empirical moments matching distribution moments.
Probability and Statistics
Covers fundamental concepts in probability and statistics, including distributions, properties, and expectations of random variables.
Probabilistic Linear Regression
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Bayesian Inference: Gaussian Variables
Explores Bayesian inference for Gaussian random variables, covering joint distribution, marginal pdfs, and the Bayes classifier.