Diffusion: Data Denoising and Generative Modeling

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Covers the theory of digital signal processing, including sampling, transformation methods, digitization, and PID controllers.

Covers the process of making pizza, sampling, averages, dispersion, residuals, and normal distribution.

Explores data representations, overfitting, model selection, Bag of Words, and learning with imbalanced data.

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

Covers data representations and processing techniques essential for effective machine learning algorithms.

Covers the importance of sampling, signal reconstruction, and aliasing in digital representation.

Explores research process, variable types, causality vs correlation, and sampling strategies.

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

Explores challenges in data assumptions, biases, and more in research, including incomplete write-ups and frustrations of newcomers.

Covers spatial sampling in GIS, including autocorrelation, elevation models, and interpolation methods.