backpropagation

It is a special case of the more general backpropagation algorithm.

A second major advantage of backpropagation networks follows from the first.

Backpropagation is an iterative process that can often take a great deal of time to complete.

The term backpropagation is short for backwards propagation of data.

The networks were trained by backpropagation from transcripts of 400 games in which the author played himself.

More recently, there has been some renewed interest in backpropagation networks due to the successes of deep learning.

This function has a continuous derivative, which allows it to be used in backpropagation.

Some researchers have argued that catastrophic interference is not an issue with the backpropagation model of human memory.

The convergence obtained from backpropagation learning is very slow.

In addition to active backpropagation of the action potential, there is also passive electrotonic spread.

Pattern-based learning is analogous to how a standard backpropagation network learns.

This is the reason why backpropagation requires the activation function to be differentiable.

Thus, pre-training is a simple way to reduce catastrophic forgetting in standard backpropagation networks.

Backpropagation is a method of training neural networks to perform tasks more accurately.

Neural network backpropagation, η stands for the learning rate.

Notably, these backpropagation networks are susceptible to catastrophic interference.

These internal representations give the backpropagation network its ability to capture abstract relationships between different input patterns.

It is commonly seen in multilayer perceptrons using a backpropagation algorithm.

The convergence in backpropagation learning is not guaranteed.

Dendritic backpropagation and the state of the awake neocortex.

These networks are commonly referred to as Backpropagation networks.

The backpropagation algorithm aims to find the set of weights that minimizes the error.

The method used in backpropagation is gradient descent.

He describes the decryption scheme and the public key creation that are based on a backpropagation neural network.

For better understanding, the backpropagation learning algorithm can be divided into two phases: propagation and weight update.