ANNs and Multilayer Perceptrons
- (Stata Center, MIT - Yu-Chih Ko)
- Overview
Artificial Neural Networks (ANNs) and MultiLayer Perceptrons (MLPs) are both types of neural networks used in machine learning (ML).
Here are some differences between ANNs and perceptrons:
- ANNs: A computational model inspired by the biological neural networks in the human brain. ANN models can be single or multi-layered. Any network with more than two layers is a deep neural network.
- Perceptrons: A neural network unit that performs computations to detect features or business intelligence in the input data. Perceptrons are a simple type of artificial neuron used for binary classification.
Perceptrons use different weights for each signal. They also use the threshold function, while adaline uses a linear activation function.
MLPs are a type of ANN with a specific architecture. MLPs are fully connected multi-layer neural networks with three layers, including one hidden layer. If it has more than one hidden layer, it is called a deep ANN. MLPs extend the concept of artificial neurons into hidden layers, enabling the modeling of complex relationships in data.
[More to come ...]
