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.
