Neural network options for simple data classification
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. 

Dropout is used for avoiding overfitting.