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Neural Networks and nnet Package in R
geeksforgeeks.org/r-language/neural-networks-using-the-r-nnet-package/Yapay zekadan makale özeti
- Neural Network Basics
- Neural networks are computational models inspired by human brain structure
- Network consists of interconnected neurons organized into layers
- Input, hidden, and output layers process data and produce predictions
- Activation functions introduce non-linearity into network outputs
- nnet Package Features
- nnet package provides functions for building and training neural networks
- nnet() function creates and trains neural networks with specified architecture
- Package offers various activation functions including sigmoid and ReLU
- Training uses optimization algorithms like gradient descent
- Classification Example
- Data preparation involves creating feature vectors and target variable
- Data split into training (70%) and testing (30%) sets
- Model trained using nnet() function with specified parameters
- Performance evaluated using confusion matrix and accuracy metrics
- Regression Example
- Data preparation creates predictor variables and response variable
- Data split into training and testing sets using caret package
- Model trained using nnet() function with linear output function
- Performance evaluated using Root Mean Squared Error (RMSE)