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Lecture
Word Embeddings: Models and Learning
Graph Chatbot
Related lectures (29)
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Probability and Statistics
Covers probability, statistics, independence, covariance, correlation, and random variables.
Probability and Statistics
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Central Limit Theorem: Properties and Applications
Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.
Law of Large Numbers: Strong Convergence
Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.
Probability and Statistics
Covers probability distributions, moments, and continuous random variables.
Probability and Statistics: Data Modeling and Analysis
Explores PDF forms, statistics, boxplots, density curves, and data analysis methods.
Binomial Distributions
Covers the normal distribution, inferential statistics, probability, and the binomial distribution in the context of the 'Dishonest Gambler Problem'.
Normal Distribution: Characteristics and Z-scores
Explores normal distribution characteristics, Z-scores, probability in inferential statistics, sample effects, and binomial distribution approximation.
Latent Semantic Indexing
Covers Latent Semantic Indexing, word embeddings, and the skipgram model with negative sampling.
