Can we use RNN for binary classification?
Yes but usually RNN works best with the time series data where past information needs to be incorporated. But if sole classification is the end goal and data is non-time series, a simple algorithm from logistic regression for binary classification should be suffice as it will reduce implementation algorithm complexity.
Can recurrent neural networks be used for classification?
Abstract–Recurrent neural networks are widely used for context dependent pattern classification tasks such as speech recognition. The feedback in these networks is generally claimed to contribute to integrating the context of the input feature vector to be classified.
Which algorithm is a part of recurrent neural network?
Recurrent neural networks leverage backpropagation through time (BPTT) algorithm to determine the gradients, which is slightly different from traditional backpropagation as it is specific to sequence data.
Why is recurrent neural network hard to train?
One of the simplest ways to explain why recurrent neural networks are hard to train is that they are not feedforward neural networks. In feedforward neural networks, signals only move one way. The signal moves from an input layer to various hidden layers, and forward, to the output layer of a system.
Which neural network is best for binary classification?
The use of a single Sigmoid/Logistic neuron in the output layer is the mainstay of a binary classification neural network. This is because the output of a Sigmoid/Logistic function can be conveniently interpreted as the estimated probability(p̂, pronounced p-hat) that the given input belongs to the “positive” class.
How difficult are neural networks?
Training deep learning neural networks is very challenging. The best general algorithm known for solving this problem is stochastic gradient descent, where model weights are updated each iteration using the backpropagation of error algorithm. Optimization in general is an extremely difficult task.