Abstract: In some embodiments, apparatuses and methods are provided herein useful for event detection relative to catalog items available for commercial sale. In some embodiments, there is provided a system for event detection relative to catalog items available for commercial sale including an interface; a memory; and the first control circuit. The first control circuit configured to execute the computer-implemented code to: receive event data corresponding to one or more events referred to in one or more online sources; determine a corresponding score for each of the event data based on a relative level of negativity; identify one or more topics and key phrases of one or more of the event data having at least a specified score; identify one or more catalog items that have a similarity to the one or more topics and key phrases; and output an indication.

Abstract: A system can implement, in a first hyperparameter configuration state, a first set of hyperparameter search operations. The first set of hyperparameter search operations includes selecting a first set of hyperparameters. Each hyperparameter of the first set of hyperparameters having a corresponding configuration. Additionally, the first set of hyperparameter search operations includes obtaining a first set of performance data that includes information indicating a performance of each hyperparameter of the first set of hyperparameters, and assigning a value to each hyperparameter of the first set of hyperparameters based on the corresponding performance data.

Abstract: This application relates to apparatus and methods for optimizing hyperparameters for machine learning models. In some examples, a computing device configures a machine learning model with a first set of hyperparameters from a pool of hyperparameters. The computing device may execute the machine learning model to generate a validation score, and may update parameters of a probability determination model based on the validation score. Further, the computing device may execute the probability determination model to generate probabilities corresponding to the first set of hyperparameters. The computing device may also determine a second set of hyperparameters from the pool of hyperparameters based on the generated probabilities, and may configure the machine learning model with the second set of hyperparameters.

Abstract: This application relates to apparatus and methods for optimizing hyperparameters for machine learning models. In some examples, a computing device configures a machine learning model with a first set of hyperparameters from a pool of hyperparameters. The computing device may execute the machine learning model to generate a validation score, and may update parameters of a probability determination model based on the validation score. Further, the computing device may execute the probability determination model to generate probabilities corresponding to the first set of hyperparameters. The computing device may also determine a second set of hyperparameters from the pool of hyperparameters based on the generated probabilities, and may configure the machine learning model with the second set of hyperparameters.