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The document discusses various optimization techniques in the context of machine learning, focusing on gradient descent, stochastic gradient descent, and mini-batch gradient descent. It also covers Maximum Likelihood Estimation (MLE) and Maximum A Posteriori (MAP) estimation, explaining their mathematical foundations and applications, particularly in Bayesian statistics. Additionally, it introduces the beta distribution and conjugate priors, illustrating how they relate to the estimation of parameters in pr
- Author
- kumarpankaja787
- Language
- EN