In Model based learning methods, an iterative process takes place on the ML models that are built based on various model parameters, called ?mini-batcheshyperparameterssuperparametersoptimizedparameters

Training the existing ML models so that they may produce well a sequence of decisions is called asSupervised methodUnsupevised methodSemi supervised methodReinforcement method

Data used to optimize the parameter settings of a supervised learner model is called ...............(1 Point)TestTrainingValidationNone of the above

What is the purpose of training an ML model? a) To evaluate the model's performance b) To preprocess the input data c) To optimize the model's parameters d) To generate predictions

What is the process of feeding a machine learning model with data to adjust its internal parameters and improve performance?Select one:a. Model trainingb. Model Validation

What is used to refine the models during training?Batch NormalizationAdam OptimizerAll of the given optionsConv2DLeakyReLU