### Neural Networks (Part 4 of 4) - R Packages and Resources

While developing these demonstrations in logistic regression and neural networks, I used and discovered some interesting methods and techniques:

### Better Methods

A few useful commands and packages...:- update.packages() for updating installed packages in one easy action
- as.formula() for creating a formula that I can reuse and update in one action across all my code sections
- View() for looking at data frames
- fourfoldplot() for plotting confusion matrices
- neuralnet for developing neural networks
- caret, used with nnet, to create predictive model
- plotnet() in NeuralNetTools, for creating attractive neural network models

### Resources that I used or that I would like to explore...

- MS Azure Notebooks, for working online with Python, R, and F#, all part of MS's data workflows
- Efficient R Programming, that seems to have many good tips on working with R
- Data Mining Algorithms in SSAS, Excel, and R, showing various algorithms in each technology
- R Documentation, a high quality, useable resource

### To explore this series...

- Neural Networks (Part 1 of 4) - Logistic Regression and neuralnet on State 'Personality' and Political Outcomes
- Neural Networks (Part 2 of 4) - caret and nnet on State 'Personality' and Political Outcomes
- Neural Networks in R (Part 3 of 4) - Neural Networks on Price Changes in Financial Data
- Attractive Confusion Matrices in R Plotted with fourfoldplot

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