# Neural network options for simple data classification

I want to clear about keras neural network options for classification of simple data where there are a number of features and one target column, as in iris flower dataset (Species is target):

SL,  SW,  PL,  PW,  Species
5.1, 3.5, 1.4, 0.2, setosa
4.9, 3.0, 1.4, 0.2, setosa
4.7, 3.2, 1.3, 0.2, setosa
...
...


I am finding that in almost all examples various combinations of Dense layers Dropout are the only options:

model = Sequential()


What other keras layers can be used in such situations, especially if the data is large, say with 50K rows and 100 features?

Edit: My specific question is whether Dense and Dropout are the only kind of layers for this purpose and such data?

You may not be very familiar with deep learning. Each kind of network is used for a special kind of task, you cannot just stack LSTMs, GRUs, dense layers and other stuff without supervision. If you have a task that your patterns are local and they may be in multiple locations in an input pattern, you can employ convolutional layers for feature extraction and you can employ dense layers for classifying those extracted features. If you want to classify data which there is a kind of sequence in it, temporal data, you can employ LSTMs and GRUs and you can stack them for better accuracy and you can use their output and feed them to other networks based on your need. MLPs are good for learning non-linear mappings.
• Very good brief description for many layers. By "you can just stack LSTMs, GRUs, dense layers and other stuff without supervision", do you mean that these can be put in most situations and data? – rnso Sep 30 '18 at 16:15