Types of NNs

See Perceptron

First layer is input, last layer output, hidden layers inbetween. Deep network: >1 hidden layer

Transformation which change the similarity of the input cases (e.g. different voiced, same words): Activity of neurons in each layer are non-linear function of the activities in the layer below.

• Directed cycles (you can get back to the neurons where you start).
• Harder to train

Natural for modeling sequential data:

• Equivalent to very deep nets with one hidden layer per time slice, except that they use same weights at every time sclice and get input at very time slice.
• Can rember info in the hidden state for a long time.

See https://wiki.movedesign.de/doku.php?id=data_mining:neural_network:sequence_learning#recurrent_neural_networks

Like RNN, but connections between units are symmetrical (same weigths in both directions).

• Easier to analyze
• Restricted: Cannot model cycles
• “Hopfield nets” if they have no hidden layer.

See https://wiki.movedesign.de/doku.php?id=data_mining:neural_network:cnn