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About 4 Multilayer Perceptrons and Radial Basis Functions
This document discusses multilayer perceptrons (MLPs) and backpropagation for training MLPs. It begins by introducing MLPs, which extend simple perceptrons by adding one or more hidden layers between the input and output layers. It then describes backpropagation, which is used to calculate weight updates for training MLPs. The key steps are: 1) perform a forward pass to calculate outputs, 2) calculate error terms for output and hidden layers, 3) use the error terms to calculate weight updates for each layer. Additional details are provided on initialization, activation functions, and practical considerations for using MLPs such as avoiding overfitting. Radial basis functions are also introduced as an alternative approach.
Detailed Information
| Author: | ['Vivek '] |
|---|---|
| Publication Year: | 2016 |
| Pages: | 6 |
| Language: | English |
| Format: | |
| Price: | FREE |
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