I'm going through a tutorial for tensorflow with keras and at one stage you build the neural network model using the code below.
model = tf.keras.Sequential([ layers.Embedding(max_features + 1, embedding_dim), layers.Dropout(0.2), layers.GlobalAveragePooling1D(), layers.Dropout(0.2), layers.Dense(1)])
I'm aware that there are different kinds of neural networks such as CNNs and RNNs which are better suited for different tasks. However, how do I relate the architecture built here to what kind of model it is?
Also if possible, if I know what type of model I want to build how does that relate to how I build the keras model?