R Deep Learning Essentials > PACKT 원서리스트

1: Getting Started with Deep Learning
What is deep learning?
R packages for deep learning
Connecting R and H2O
Summary

2: Training a Prediction Model
Neural networks in R
The problem of overfitting data – the consequences explained
Use case – build and apply a neural network
Summary

3: Preventing Overfitting
L1 penalty
L2 penalty
Ensembles and model averaging
Use case – improving out-of-sample model performance using dropout
Summary

4: Identifying Anomalous Data
Getting started with unsupervised learning
How do auto-encoders work?
Training an auto-encoder in R
Use case – building and applying an auto-encoder model
Fine-tuning auto-encoder models
Summary

5: Training Deep Prediction Models
Getting started with deep feedforward neural networks
Common activation functions – rectifiers, hyperbolic tangent, and maxout
Picking hyperparameters
Training and predicting new data from a deep neural network
Use case – training a deep neural network for automatic classification
Summary

6: Tuning and Optimizing Models
Dealing with missing data
Solutions for models with low accuracy
Summary
appA: Appendix A: Bibliography
backindex: Appendix B: Index