MLG 028 Hyperparameters 2

This podcast episode explores various techniques and concepts related to hyperparameter optimization, regularization, dropout, optimizers, and feature scaling in machine learning. It covers grid search, random search, and Bayesian optimization as hyperparameter optimization techniques. The differences between grid search and random search, k-fold cross-validation, and the concept of "turtles all the way down" in hyperparameter optimization are also discussed. The importance of regularization, including L1 and L2 regularization, as well as dropout in preventing overfitting is explained. The evolution of optimizers, such as momentum, Nesterov, and decaying learning rate, is explored. Finally, the significance of weight initialization, feature scaling techniques like standardization and normalization, and the use of optimization techniques for feature scaling are highlighted.