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Related lectures (30)

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Introduces BlinkDB, a framework for approximate query processing using sampling techniques.

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.

Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.

Covers generative models, focusing on Boltzmann machines and constrained maximization using Lagrange multipliers.

Covers determinantal point processes, sine-process, and their extrapolation in different spaces.

Covers Markov chains and their applications in algorithms, focusing on Markov Chain Monte Carlo sampling and the Metropolis-Hastings algorithm.

Covers the theory and applications of graph coloring, focusing on disassortative stochastic block models and planted coloring.

Explores challenges in natural language generation, decoding algorithms, training issues, and reward functions.

Explores the characterization of powders in ceramics, emphasizing the impact on ceramic properties and the manufacturing process.

Explores sampling, inferential statistics, and effective experimentation in statistics.