By Dr. Joshua F. Wiley
- Harness the power to construct algorithms for unsupervised info utilizing deep studying techniques with R
- Master the typical difficulties confronted equivalent to overfitting of knowledge, anomalous datasets, snapshot reputation, and function tuning whereas development the models
- Build versions in terms of neural networks, prediction and deep prediction
Deep studying is a department of computer studying according to a suite of algorithms that try and version high-level abstractions in info through the use of version architectures. With the excellent reminiscence administration and the total integration with multi-node mammoth information structures, the H2O engine has turn into an increasing number of well known between info 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 suggestions of deep studying. we are going to commence by means of establishing very important deep studying applications on hand in R after which stream in the direction of development versions regarding neural networks, prediction, and deep prediction, all of this with assistance from real-life examples.
After fitting the H2O package deal, you'll know about prediction algorithms. relocating forward, options reminiscent of overfitting info, anomalous facts, and deep prediction versions are defined. ultimately, the publication will conceal thoughts on the subject of tuning and optimizing models.
What you'll learn
- Set up the R package deal H2O to coach deep studying models
- Understand the center thoughts at the back of deep studying models
- Use Autoencoders to spot anomalous information or outliers
- Predict or classify information immediately utilizing deep neural networks
- Build generalizable types utilizing regularization to prevent overfitting the learning data
About the Author
Dr. Joshua F. Wiley is a lecturer at Monash college and a senior companion at Elkhart crew restricted, a statistical consultancy. He earned his PhD from the college of California, l. a.. His study makes a speciality of utilizing complicated quantitative easy methods to comprehend the complicated interplays of mental, social, and physiological methods on the subject of mental and actual health and wellbeing. In records and knowledge technological know-how, Joshua makes a speciality of biostatistics and is drawn to reproducible examine and graphical monitors of information and statistical types. via consulting at Elkhart team constrained and his former paintings on the UCLA Statistical Consulting crew, Joshua has helped a big selection of consumers, starting from skilled researchers to biotechnology businesses. He develops or codevelops a couple of 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
- Getting began with Deep Learning
- Training a Prediction Model
- Preventing Overfitting
- Identifying Anomalous Data
- Training Deep Prediction Models
- Tuning and Optimizing Models