So Why is Data Science important for Deep Learning?

Most Deep Learning AI projects are actually Data Science projects. First one needs the Data to train the models. Data accessibility is very important. Data also needs to be prepared for Deep Learning. Data Science programming languages such as python and libraries such as tensorflow and pytorch are the core of Deep Learning implementations.

Train / Validate / Test methodology. The methodology in which the Dataset is split into Train, Validation and Test sets is the core of training and tuning the Deep Learning models. This involves substantial Data Science knowledge and is the core of any AI mode engineering projects.

For Deep Learning models to be deployed, some data is again fed into them as input to make predications . Data Science is for this reason one of the most important segments of any Deep Learning projects.

Evaluation of Deep Learning models performance is done use various forms of Data Science and Statistics. Special metrics are calculated to see how Deep Learning perform on new real world data which was not part of the Training process to see how accurate are the predictions.

There metrics may be Accuracy, Sensitivity, Specificity and ROC curves for Classification tasks. Confusion matrix may also be constructed to evaluate the model. For regression tasks these metrics may be MSE (mean squared error) RMSE (Root Mean Squared Error) and MAE (Mean absolute Error).