The video tutorial explains the concept of deep neural networks (DNNs), focusing on a model with three hidden layers. It covers the process of building a fully connected DNN, implementing it on testing data, and evaluating its performance using a confusion matrix. The tutorial demonstrates how to calculate overall accuracy and other metrics, achieving an accuracy of over 88% in predicting unseen data labels.

Explain the process of how the DNN model was implemented on the testing data.

What metrics can be derived from the confusion matrix besides overall accuracy?