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R Deep Learning Cookbook

You're reading from   R Deep Learning Cookbook Solve complex neural net problems with TensorFlow, H2O and MXNet

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787121089
Length 288 pages
Edition 1st Edition
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Authors (2):
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Achyutuni Sri Krishna Rao Achyutuni Sri Krishna Rao
Author Profile Icon Achyutuni Sri Krishna Rao
Achyutuni Sri Krishna Rao
PKS Prakash PKS Prakash
Author Profile Icon PKS Prakash
PKS Prakash
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Toc

Table of Contents (11) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Deep Learning with R 3. Convolution Neural Network 4. Data Representation Using Autoencoders 5. Generative Models in Deep Learning 6. Recurrent Neural Networks 7. Reinforcement Learning 8. Application of Deep Learning in Text Mining 9. Application of Deep Learning to Signal processing 10. Transfer Learning

Performing logistic regression using H2O


Generalized linear models (GLM) are widely used in both regression- and classification-based predictive analysis. These models optimize using maximum likelihood and scale well with larger datasets. In H2O, GLM has the flexibility to handle both L1 and L2 penalties (including elastic net). It supports Gaussian, Binomial, Poisson, and Gamma distributions of dependent variables. It is efficient in handling categorical variables, computing full regularizations, and performing distributed n-fold cross validations to control for model overfitting. It has a feature to optimize hyperparameters such as elastic net (α) using distributed grid searches along with handling upper and lower bounds for predictor attribute coefficients. It can also handle automatic missing value imputation. It uses the Hogwild method for optimization, a parallel version of stochastic gradient descent.

Getting ready

The previous chapter provided the details for the installation of H2O...

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