1.Diverse data can be machine learned.
MLP uses error back propagation to set weights on the input data and adjust them to minimize the error with the output data. This allows for proper machine learning on a wide variety of data.
Error back propagation method: One of the algorithms used to train neural networks. By propagating the error calculated in the output layer in the reverse direction, each weight in the network is adjusted to minimize the error. This allows the network to more accurately predict the output for the input data.
2.Can predict unknown data.
MLP can reason about regression problems and can predict future data from past data. For example, one can predict next year's sales from past sales data, based on a series of consecutive numbers.
3.Can solve complex problems.
MLPs are capable of complex classifications because they have multiple intermediate layers. Problems that could only be handled linearly with a simple perceptron can be classified curvilinearly with a multi-layer perceptron, allowing for more complex problems to be solved.
Perceptron: One of the most basic components of a neural network. A simple perceptron consists of an input layer and an output layer and is an algorithm for solving linearly separable problems. It operates with a single neuron, calculates a weighted sum over the inputs, and compares the result to a threshold to determine the output.
