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Neural Network: Multi-layer Perceptron (MLP)
#30
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sophryu99
opened
3 years ago
sophryu99
commented
3 years ago
Multi-layer Perceptron (MLP)
MLP
is Class of
feedforward artificial neural network (ANN).
Composed of multiple layers of perceptrons.
MLP consists of at least three layers of nodes: an input layer, a hidden layer, and an output layer
Except for the input nodes, each node is a neuron that uses a
nonlinear
activation function.
Without the activation function, the model can be classified as a logistic regression
Utilizes a supervised learning called back-propagation #29
Types of Layers in MLP
Input Layer
: Input variables, sometimes called the visible layer.
Hidden Layers
: Layers of nodes between the input and output layers. There may be one or more of these layers.
Output Layer
: A layer of nodes that produce the output variables.
sophryu99
commented
3 years ago
Terminologies to describe the shape and capability of a neural network
Size
: The number of nodes in the model.
Width
: The number of nodes in a specific layer.
Depth
: The number of layers in a neural network.
Capacity
: The type or structure of functions that can be learned by a network configuration. Sometimes called “
representational capacity
“.
Architecture
: The specific arrangement of the layers and nodes in the network.
Batch Size
: Number of instances that will be processed for each back propagation run
Epoch
: One run through the whole dataset
Stopping criteria
: The error changes less than epsilon
Rules of thumb in network design
:
Multi-layer Perceptron (MLP)
MLP is Class of feedforward artificial neural network (ANN).
Types of Layers in MLP