SWISH: Split Where Information Should Happen

Related lectures (32)

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Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.

Covers the distribution of data in the FlowField class, focusing on gridpoint calculations and MPI usage.

Discusses challenges in scalable analytics over heterogeneous Big Data and introduces SmartDataLake for efficient handling of raw data.

Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.

Explores copulas, simulation algorithms, fitting data with rank correlations, and GANs for image generation.

Explores the evolutionary perspective on surprise, curiosity, and reward, focusing on the role of primary and secondary reward signals.

Explores the benefits, challenges, and applications of self-organization in various fields.

Covers analytical query processing, declustering strategies, and distributed operations.

Covers Extreme Value Theory, focusing on GEV and GPD distributions and the POT Model for threshold exceedances.

Explores social influence, democracy, individual autonomy, equality, and decision-making models.