Training a neural network is an iterative process, which involves discovering values for its weights and its bias terms. These are used in conjunction with the input values to create outputs. After much iteration, the model is tested for the purposes of becoming a full production model that can be used to make predictions.
Training a neural network model is an iterative process, which is a key part of the Cross Industry Standard Process for Data Mining (CRISP-DM) as an integral part of the modeling phase. Training involves working out weights and bias values that lead the inputs towards the preferred output. As part of the training process, the model can be presented with the test data in order to evaluate its accuracy. This will help us to understand how well the model will perform when it is given new data, and we don't know the true output results.
During the training process, rows are presented to the neural network consecutively, one at...