What is the difference between keras LSTM network and neural network?
Whereas lstm network also known as RNN (Recurrent neural network) is a bit critical concept as it involves traversal of data in cycles format via different layers. In other words, keras lstm with RNN neural network leverages the facility and ability to compare sequential data dynamically to store the stuff that has been predicted.
What is dropout Keras in recurrent neural networks?
Recurrent Neural networks like LSTM generally have the problem of overfitting. Dropout can be applied between layers using the Dropout Keras layer. We can do this easily by adding new Dropout layers between the Embedding and LSTM layers and the LSTM and Dense output layers. For example:
Which algorithm is used for binary classification in keras?
Because it is a binary classification problem, log loss is used as the loss function ( binary_crossentropy in Keras). The efficient ADAM optimization algorithm is used.
What is the accuracy of Keras model in stock market?
The accuracy as obtained on the training data-set is about 90 percent and it successfully demonstrates key trends. It can be simulated on any stock in the market provided their historical data is made available. (One could use the yfinance API or download manually). Keras is used extensively along with Tensorflow for training.