Python Deep Learning Projects

Defining the model and training

Now, we are getting to the part that all deep learning engineers love: designing the model's architecture!  We will be using four distinctive types of layers in our model architecture:

• LSTM: This is a type of RNN layer.
• Dropout: A technique for regularization. This helps prevent the model from overfitting by randomly dropping some nodes.
• Dense: This is a fully connected layer where every input node is connected to every output node.
• Activation: This determines the activation function that's going to be used to produce the node's output.

We will again employ the Keras APIs to make the implementation quick:

model = Sequential()model.add(LSTM(    256, input_shape=(network_input.shape[1], network_input.shape[2]), ...