Storage Capacity: Prototypes and Neuronal Dynamics

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Introduces probability theory, random variables, and distributions, with a focus on their applications in atomic diffusion.

Covers broadcasting, operations, comparisons, and numpy constants like pi, e, and infinity.

Explains statistical methods in quantitative analysis, emphasizing precision, accuracy, and sample representation.

Explores the dynamics of associative memory in networks of neurons and includes a detour into magnetic materials.

Explains data dispersion, central tendency, variance, standard deviation, and Mean Square Error.

Covers statistical thematic mapping, types of maps, discretization methods, and proportional symbols in maps.

Explores associative memory in neuronal networks, neuronal structure, and information processing.

Covers significant figures, notation for derivatives, and error estimation methods.

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Explores probability theory, distribution of averages, and the central limit theorem.