In general, perceptrons and neural networks need a way to tune their weights and biases to improve performance. Performance is measured by a loss function. This function tells the network how it ...

The idea here is that you consider the entire neural network and its loss function as a multivariate (multidimensional) equation depending on the weights and biases. You begin at the output ...

Next, the demo creates an 8-(10-10)-1 deep neural network. The demo prepares training by setting up a loss function (mean squared error), a training optimizer function (Adam) and parameters for ...

During training, a neural network settled on the best weights for its neurons ... To understand this process, think of a “loss function” that describes the difference between the inferred and desired ...