What is NNET?

What is NNET?

A neural network classifier is a software system that predicts the value of a categorical value. The R language has an add-on package named nnet that allows you to create a neural network classifier.

What is size and decay in NNET?

Size is the number of units in hidden layer (nnet fit a single hidden layer neural network) and decay is the regularization parameter to avoid over-fitting.

What activation function does NNET use?

Most references I find say that the activation function used in nnet is ‘usually’ a logistic function.

What is weight decay in Bert?

Often weight decay refers to the implementation where we specify it directly in the weight update rule (whereas L2 regularization is usually the implementation which is specified in the objective function). …

What is weight decay in SGD?

Weight decay is a regularization technique by adding a small penalty, usually the L2 norm of the weights (all the weights of the model), to the loss function. loss = loss + weight decay parameter * L2 norm of the weights. Some people prefer to only apply weight decay to the weights and not the bias.

What is the problem with RNNs and gradients?

However, RNNs suffer from the problem of vanishing gradients, which hampers learning of long data sequences. The gradients carry information used in the RNN parameter update and when the gradient becomes smaller and smaller, the parameter updates become insignificant which means no real learning is done.

How does ReLU introduce non-linearity?

Definitely it is not linear. As a simple definition, linear function is a function which has same derivative for the inputs in its domain. ReLU is not linear. The simple answer is that ReLU ‘s output is not a straight line, it bends at the x-axis.

Is SGD better than Adam?

Adam is great, it’s much faster than SGD, the default hyperparameters usually works fine, but it has its own pitfall too. Many accused Adam has convergence problems that often SGD + momentum can converge better with longer training time. We often see a lot of papers in 2018 and 2019 were still using SGD.

Is Adam the best optimizer?

Adam is the best among the adaptive optimizers in most of the cases. Good with sparse data: the adaptive learning rate is perfect for this type of datasets.

Is AdamW better than Adam?

The authors show experimentally that AdamW yields better training loss and that the models generalize much better than models trained with Adam allowing the new version to compete with stochastic gradient descent with momentum.

What is decay in CNN?

Edit. Weight Decay, or Regularization, is a regularization technique applied to the weights of a neural network. We minimize a loss function compromising both the primary loss function and a penalty on the Norm of the weights: L n e w ( w ) = L o r i g i n a l ( w ) + λ w T w.

Does LSTM solve exploding gradient?

Although LSTMs tend to not suffer from the vanishing gradient problem, they can have exploding gradients. I have always thought that RNNs with LSTM units solve both the “vanishing” and “exploding gradients” problems, but, apparently, RNNs with LSTM units also suffer from “exploding gradients”.

https://www.youtube.com/watch?v=qF3NtuN3iTk