What are different types of Artificial Neural Networks?

1.Feedforward Neural Network: Neural Network that the cells do not form a cycle.

Feedforward Neural Network

1.1.Multilayer Perceptron: Feedforwrd Neural Network with perceptrons organized into input, hidden and output layers.

1.1.1.Convolutional Neural Network: Multilayer Perceptron with convolutional layers.

Conolutional Neural Network

1.2.Autoencoder: Feedforward Neural Network with equal number of inputs and outputs trained to replicate the input.

Autoencoder Neural Network

2.Recurrent Neural Network: Neural Network that have access to their output from previous time steps.

Recurrent Neural Network

2.1.LSTM Recurrent Neural Network: Long Short Term Recurrent Neural Networks are Recurrent Neural Networks with LSTM cells with memory.

LSTM Neural Network

2.2.GRU Recurrent Neural Network: Gated Recurrent Unit Neural Networks are Recurrent Neural Networks with GRU cells.

GRU Neural Network

2.3.Neural Turing Machine: Recurrent Neural Network with neural network controller coupled to external memory.

3.Transformer: Neural Network with a structure of encoder and/or decoder with presence of attention mechanism that is predominantly used in Natural Language Processing.

4.Stochastic Neural Network: Neural Network with random variations introduced to the network via stochastic transfer functions or stochastic weights.

4.1.Restricted Boltzman Machine: Neural Network consisting of input and hidden cells trained to learn the probability distribution of its set of inputs.

4.2.Deep Belief Network: Neural Network consisting of stacking of Restricted Boltzman Machines and their training in a greedy manner.

5.Generative Adverserial Networks: Combination of a generative network and a discriminative network in a competing training.

GAN Neural Network