Deploying Prediction Models

  Stock Price Prediction (Apple) with SimpleRNN Recurrent neural networks (RNN) are a class of neural networks that is powerful for modeling sequence data such as time series or natural language. Schematically, a RNN layer uses a for loop to iterate over the timesteps of a sequence, while maintaining an internal state that encodes information about the timesteps it has seen so far. In this exercise I'm going to use  keras.layers.SimpleRNN , a fully-connected RNN where the output from previous timestep is to be fed to next timestep. By default, the output of a RNN layer contains a single vector per sample. This vector is the RNN cell output corresponding to the last timestep, containing information about the entire input sequence. The shape of this output is (batch_size, units) where units corresponds to the units argument passed to the layer's constructor. A RNN layer can also return the entire sequence of outputs for each sample (one vector per timestep per sample), if...