backpropagation in a sentence

1) The convergence obtained from backpropagation learning is very slow.

2) The method used in backpropagation is gradient descent.

3) The convergence in backpropagation learning is not guaranteed.

4) This article provides details on how backpropagation works.

backpropagation example sentences

5) Notably, these backpropagation networks are susceptible to catastrophic interference.

6) In Backpropagation, each calculation is divided into two pass.

7) They can also be trained with standard backpropagation.

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

9) See my own " A Gentle Introduction to backpropagation ".

10) The most common algorithm used in Neural Networks is called Backpropagation.

11) In my case, backpropagation does not work so well.

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

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

14) The backpropagation learning algorithm can be divided into two phases: propagation and weight update.

example sentences with backpropagation

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

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

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

18) backpropagation learning does not require normalization of input vectors; however, normalization could improve performance.

19) backpropagation or other discriminative algorithms can then be applied for fine-tuning of these weights.

20) Well-learned information was catastrophically forgotten as new information was learned in both small and large backpropagation networks.

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

22) And backpropagation is a machine learning technique that was developed in the eighties for learning feed-forward neural networks.

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

24) Also key later advances was the backpropagation algorithm which effectively solved the exclusive-or problem (Werbos 1975).

25) backpropagation requires a known, desired output for each input value in order to calculate the loss function gradient.

26) So deep learning is a collection of machine learning techniques developed in response to problems people found with backpropagation.

27) In the case of a three-layer backpropagation network, the response function is a non-linear, logistic function.

How to use backpropagation in a sentence

28) For multilayer perceptrons, where a hidden layer exists, more sophisticated algorithms such as backpropagation must be used.

29) When multicore computers are used multithreaded techniques can greatly decrease the amount of time that backpropagation takes to converge.

30) Technically speaking, backpropagation calculates the gradient of the error of the network regarding the network's modifiable weights.

31) backpropagation requires that the activation function used by the artificial neurons (or "nodes") be differentiable.

32) Nonlinear PCA (NLPCA) uses backpropagation to train a multi-layer perceptron to fit to a manifold.

33) Backpropagation, specifically refers to how this the network is trained, i.e. how the network is told to learn.

34) backpropagation networks are necessarily multilayer perceptrons (usually with one input, one hidden, and one output layer).

35) Other important early researchers were Frank Rosenblatt, who invented the perceptron and Paul Werbos who developed the backpropagation algorithm.

36) Backpropagation, first made popular in the 1980s, is probably the most commonly known connectionist gradient descent algorithm today.

37) McCloskey and Cohen(1989) noted the problem of catastrophic interference during two different experiments with backpropagation neural network modelling.

38) If batching is being used, it is relatively simple to adapt the backpropagation algorithm to operate in a multithreaded manner.

39) backpropagation usually allows quick convergence on satisfactory local minima for error in the kind of networks to which it is suited.

40) The weight update algorithm differs from backpropagation in that the terms P(1)P(0) are dropped.

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