Electron Transfer Theory

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Related lectures (31)

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Advanced Probabilities: Random Variables & Expected Values

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

Random Variables and Expected Value

Introduces random variables, probability distributions, and expected values through practical examples.

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Probability and Statistics

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

Fundamental Limits of Gradient-Based Learning

Delves into the fundamental limits of gradient-based learning on neural networks, covering topics such as binomial theorem, exponential series, and moment-generating functions.

Continuous Random Variables

Covers continuous random variables, probability density functions, and distributions, with practical examples.

Discrete Random Variables

Covers properties and transformations of discrete random variables, focusing on PMF and expectation.

Probability and Statistics: Discrete Random Variables

Explores discrete random variables, mass functions, and distribution functions in probability and statistics.

Statistics for Data Science: Basics and Modelling

Introduces statistical modelling basics, probability theory, and key concepts for data science applications.

Statistical Inference: Random Variables

Covers random variables, probability functions, expectations, variances, and joint distributions.