Code associated with these tutorials can be downloaded from here: https://github.com/bnsreenu/python_for_image_processing_APEER
This video explains the primary difference between training, testing, and validation data.
Training data:
Data used to fit the model.
The model sees and learns from this data.
Accuracy gets checked and reported after each iteration.
Validation data:
Data used to evaluate a model during training.
Accuracy gets checked and reported after each epoch.
Model does not update any weights or biases (learn) based on this data.
The machine learning engineer monitors the validation error and tunes hyperparameters to optimize the model for better performance.
Test data:
Data used to evaluate the final trained model.
The machine learning engineer checks the accuracy after the entire training is done.
Often validation dataset is used as test dataset.
It is best practice to work with test data that represents generalized scenarios of future data.
