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Delves into the fundamental limits of gradient-based learning on neural networks, covering topics such as binomial theorem, exponential series, and moment-generating functions.
Covers Markov processes, transition densities, and distribution conditional on information, discussing classification of states and stationary distributions.
Explores Gaussian random vectors and their statistical properties, emphasizing the importance of specifying statistical properties in complex valued random vectors.
Explores generating Gaussian random vectors with specific components based on observed values and explains the concept of positive definite covariance functions in Gaussian processes.
