By Dr. Joshua F. Wiley

Key Features

  • Harness the power to construct algorithms for unsupervised facts utilizing deep studying strategies with R
  • Master the typical difficulties confronted comparable to overfitting of information, anomalous datasets, snapshot attractiveness, and function tuning whereas construction the models
  • Build types in terms of neural networks, prediction and deep prediction

Book Description

Deep studying is a department of computing device studying according to a collection of algorithms that try and version high-level abstractions in facts by utilizing version architectures. With the excellent reminiscence administration and the whole integration with multi-node enormous facts structures, the H2O engine has develop into a growing number of renowned between information scientists within the box of deep learning.

This booklet will introduce you to the deep studying package deal H2O with R and assist you comprehend the options of deep studying. we'll begin by way of constructing very important deep studying applications to be had in R after which circulate in the direction of development types on the topic of neural networks, prediction, and deep prediction, all of this with assistance from real-life examples.

After fitting the H2O package deal, you are going to know about prediction algorithms. relocating forward, recommendations equivalent to overfitting facts, anomalous facts, and deep prediction versions are defined. eventually, the booklet will hide recommendations in terms of tuning and optimizing models.

What you are going to learn

  • Set up the R package deal H2O to coach deep studying models
  • Understand the middle strategies in the back of deep studying models
  • Use Autoencoders to spot anomalous information or outliers
  • Predict or classify facts immediately utilizing deep neural networks
  • Build generalizable versions utilizing regularization to prevent overfitting the learning data

About the Author

Dr. Joshua F. Wiley is a lecturer at Monash collage and a senior companion at Elkhart team constrained, a statistical consultancy. He earned his PhD from the collage of California, la. His learn specializes in utilizing complicated quantitative how you can comprehend the advanced interplays of mental, social, and physiological approaches with regards to mental and actual wellbeing and fitness. In information and information technology, Joshua makes a speciality of biostatistics and is drawn to reproducible learn and graphical screens of information and statistical types. via consulting at Elkhart workforce constrained and his former paintings on the UCLA Statistical Consulting team, Joshua has helped a big selection of consumers, starting from skilled researchers to biotechnology businesses. He develops or codevelops a few R programs together with varian, a package deal to behavior Bayesian scale-location structural equation versions, and MplusAutomation, a well-liked package deal that hyperlinks R to the economic Mplus software.

Table of Contents

  1. Getting begun with Deep Learning
  2. Training a Prediction Model
  3. Preventing Overfitting
  4. Identifying Anomalous Data
  5. Training Deep Prediction Models
  6. Tuning and Optimizing Models
  7. Bibliography

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