LSTM is an enhancement used to improve the performance of a Recurrent Neural Network
The Recurrent Neural Network (RNN) is designed to work with data that naturally exists as part of a sequence
In Neural Networks, a batch is a subset of the training data that the network sees before the parameters are updated.
Dropout refers to randomly turning off hidden units so that a smaller network is trained on a given pass
One of the main drawbacks of deep learning is that it is more prone to overfitting
The following are some options that have been shown to reduce the risk of experiencing a vanishing or exploding gradient
The vanishing or exploding gradient is an issue often encountered in the training of deep Neural Networks.
In a deep network with many hidden layers, it can be very computationally intensive to compute derivatives of all of the parameters of the network.
In Backwards Propagation, the parameters of a Neural Network (all of the weight and bias terms) are updated using a gradient descent optimization
Multilayer perceptrons models are suitable for both regression and classification tasks