Multilayer Perceptron
Last updated
Last updated
Can draw complex non-linear boundaries to separate data
But needs more data!
Fully-connected feedforward neural network
In the output layer:
For classification, is commonly used (even for binary classification problems)
However, can be used as well for 1/0 classification.
In the hidden layer,
we need to use a non-linear activation function
we can use the
but it's more common to use the nowadays
In theory, an MLP with 1 hidden layer should be enough. But:
Needs lots of hidden units (wide & shallow)
Prone to overfitting
A narrow and deep MLP:
needs fewer nodes and generalizes better
but, it's harder to train!
Cannot initialize the weights to 0, to avoid losing the power of the different hidden units.
Random initialization:
To small and random numbers!
To keep all hidden layers with different numbers
It can be seen as a model with Hidden layers:
