Tweet Share Share Google Plus A problem with training neural networks is in the choice of the number of training epochs to use. Too many epochs can lead to overfitting of the training datase... Read more

Tweet Share Share Google Plus A major challenge in training neural networks is how long to train them. Too little training will mean that the model will underfit the train and the test sets.... Read more

Tweet Share Share Google Plus Dropout regularization is a computationally cheap way to regularize a deep neural network. Dropout works by probabilistically removing, or “dropping out,” input... Read more

Tweet Share Share Google Plus Deep learning neural networks are likely to quickly overfit a training dataset with few examples. Ensembles of neural networks with different model configuratio... Read more

Tweet Share Share Google Plus Activity regularization provides an approach to encourage a neural network to learn sparse features or internal representations of raw observations. It is commo... Read more

Tweet Share Share Google Plus Deep learning models are capable of automatically learning a rich internal representation from raw input data. This is called feature or representation learning... Read more

Tweet Share Share Google Plus Weight constraints provide an approach to reduce the overfitting of a deep learning neural network model on the training data and improve the performance of the... Read more

Tweet Share Share Google Plus Weight regularization methods like weight decay introduce a penalty to the loss function when training a neural network to encourage the network to use small we... Read more

Tweet Share Share Google Plus Weight regularization provides an approach to reduce the overfitting of a deep learning neural network model on the training data and improve the performance of... Read more

Tweet Share Share Google Plus Neural networks learn a set of weights that best map inputs to outputs. A network with large network weights can be a sign of an unstable network where small ch... Read more