Feedforward Neural Network
- The simplest form of ANN, where the data or the input travels in one direction.
- The data passes through the input nodes and exit on the output nodes. This neural network may or may not have the hidden layers.
Convolutional Neural Network - Here, input features are taken in batch wise like a filter. This will help the network to remember the images in parts and can compute the operations.
- Mainly used for signal and image processing
Recurrent Neural Network(RNN) – Long Short Term Memory
- Works on the principle of saving the output of a layer and feeding this back to the input to help in predicting the outcome of the layer.
- Here, you let the neural network to work on the front propagation and remember what information it needs for later use
This way each neuron will remember some information it had in the previous time-step.
Autoencoders
- These are unsupervised learning models with an input layer, an output layer and one or more hidden layers connecting them.
- The output layer has the same number of units as the input layer. Its purpose is to reconstruct its own inputs.
- Typically for the purpose of dimensionality reduction and for learning generative models of data.