The target block represents the output that we are trying to learn with our model. As for the input block, after placing a target block we have to assign a feature to it; typically a single variable is used as output, be it a label (classification), a scalar (regression) or an image (autoencoders, image segmentation).
Feature: the feature assigned to the Target block. This can be an imported feature or a combined feature.
Loss function: the loss function to be used in training (see the documentation for loss functions).
Stay in the know by signing up for occasional emails with tips, tricks, deep learning insights, product updates, event news and webinar invitations.
We promise not to spam you or share your email with any third party. You can change your preferences at any time. See our privacy policies.
Please check your email inbox account to confirm, set, or update your communication preferences.