Does back-propagation learning algorithm guarantee to find the global optimum solution?

Summary: Back-prop is a heuristic. It cannot guarantee to find the globally optimal solution.

What does back-propagation for neural network determines?

In fitting a neural network, backpropagation computes the gradient of the loss function with respect to the weights of the network for a single input–output example, and does so efficiently, unlike a naive direct computation of the gradient with respect to each weight individually.

What is the main purpose of the backpropagation?

Backpropagation (backward propagation) is an important mathematical tool for improving the accuracy of predictions in data mining and machine learning. Essentially, backpropagation is an algorithm used to calculate derivatives quickly.

What is generalization in back propagation training algorithm?

The backpropagation neural network learning algorithm is generalized to include complex-valued interconnections for possible optical implementations. This generalization is directed toward optical implementations in which the nonlinear operation in a neuron is a function only of the optical intensity at the neuron.

What is the back propagation in NEUR networks the setting of this back to the back to pass the network to be able to pass the issue to pass the individuals?

What is back propagation? Explanation: Back propagation is the transmission of error back through the network to allow weights to be adjusted so that the network can learn. Explanation: RNN (Recurrent neural network) topology involves backward links from output to the input and hidden layers.

How weights are updated in neural networks?

A single data instance makes a forward pass through the neural network, and the weights are updated immediately, after which a forward pass is made with the next data instance, etc.