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Explores bit error rate and receiver sensitivity in optical communication systems, covering BER, receiver sensitivity, probability density functions, and error probability calculations.
Explores loss functions, gradient descent, and step size impact on optimization in machine learning models, highlighting the delicate balance required for efficient convergence.
Explores the Decision Theory Framework in Statistical Theory, viewing statistics as a random game with key concepts like admissibility, minimax rules, and Bayes rules.
