Backpropagation requires a known, desired output for each input value in order to calculate the loss function gradient; it is therefore usually considered to be a supervised learning method, although it is also used in some unsupervised networks such as autoencoders.
It is therefore usually considered to be a supervised learning method, although it is also used in some unsupervised networks such as autoencoders. Backpropagation requires a known, desired output for each input value in order to calculate the loss function gradient.
Backpropagation requires a known, desired output for each input value in order to calculate the loss function gradient. It is therefore usually considered to be a supervised learning method, although it is also used in some unsupervised networks such as autoencoders.
Backpropagation requires a known, desired output for each input value in order to calculate the loss function gradient. It is therefore usually considered to be a supervised learning method, although it is also used in some unsupervised networks such as autoencoders.