Stochastic Models for Communications

Related lectures (30)

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Explores reciprocal trigonometric functions, hyperbolic functions, and area theorems.

Covers the Laplace equation, decomposition of linear problems, and solutions through separation of variables.

Explores integration techniques, including indefinite integrals and variable changes, through trigonometric functions.

Covers the Poisson process, a stochastic model for communications, focusing on the probability law.

Covers the theory of harmonic response, including decomposition into simple elements and stable system behavior.

Covers the Poisson process in detail, focusing on the probability law and its applications.

Covers the concept of continuity, providing examples and definitions of continuous functions.

Explores differentiability, limits, and open sets in multivariable functions, with a focus on local minimums and continuity.

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.