R Deep Learning Essentials

Publisher :

ISBN-13 : 9781785280580

Page : 170 pages

Rating : 4.5/5 from 580 voters

Build automatic classification and prediction models using unsupervised learningAbout This Book- Harness the ability to build algorithms for unsupervised data using deep learning concepts with R- Master the common problems faced such as overfitting of data, anomalous datasets, image recognition, and performance tuning while building the models- Build models relating to neural networks, prediction and deep predictionWho This Book Is ForThis book caters to aspiring data scientists who are well versed with machine learning concepts with R and are looking to explore the deep learning paradigm using the packages available in R. You should have a fundamental understanding of the R language and be comfortable with statistical algorithms and machine learning techniques, but you do not need to be well versed with deep learning concepts.What You Will Learn- Set up the R package H2O to train deep learning models- Understand the core concepts behind deep learning models- Use Autoencoders to identify anomalous data or outliers- Predict or classify data automatically using deep neural networks- Build generalizable models using regularization to avoid overfitting the training dataIn DetailDeep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures. With the superb memory management and the full integration with multi-node big data platforms, the H2O engine has become more and more popular among data scientists in the field of deep learning.This book will introduce you to the deep learning package H2O with R and help you understand the concepts of deep learning. We will start by setting up important deep learning packages available in R and then move towards building models related to neural networks, prediction, and deep prediction, all of this with the help of real-life examples.After installing the H2O package, you will learn about prediction algorithms. Moving ahead, concepts such as overfitting data, anomalous data, and deep prediction models are explained. Finally, the book will cover concepts relating to tuning and optimizing models.Style and approachThis book takes a practical approach to showing you the concepts of deep learning with the R programming language. We will start with setting up important deep learning packages available in R and then move towards building models related to neural network, prediction, and deep prediction - and all of this with the help of real-life examples.

More Books:

R Deep Learning Essentials
Language: en
Pages: 170
Authors: Joshua F. Wiley
Categories: Computers
Type: BOOK - Published: 2016-03-29 - Publisher:

Build automatic classification and prediction models using unsupervised learningAbout This Book- Harness the ability to build algorithms for unsupervised data u
R Deep Learning Essentials
Language: en
Pages: 378
Authors: Mark Hodnett
Categories: Computers
Type: BOOK - Published: 2018-08-24 - Publisher: Packt Publishing Ltd

Implement neural network models in R 3.5 using TensorFlow, Keras, and MXNet Key Features Use R 3.5 for building deep learning models for computer vision and tex
R Machine Learning Essentials
Language: en
Pages: 218
Authors: Michele Usuelli
Categories: Computers
Type: BOOK - Published: 2014-11-28 - Publisher: Packt Publishing Ltd

If you want to learn how to develop effective machine learning solutions to your business problems in R, this book is for you. It would be helpful to have a bit
Machine Learning Essentials
Language: en
Pages: 209
Authors: Alboukadel Kassambara
Categories:
Type: BOOK - Published: 2018-03-10 - Publisher: STHDA

Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques. This book presents an easy to use pract
R Deep Learning Projects
Language: en
Pages: 258
Authors: Yuxi (Hayden) Liu
Categories: Mathematics
Type: BOOK - Published: 2018-02-22 - Publisher: Packt Publishing Ltd

5 real-world projects to help you master deep learning concepts Key Features Master the different deep learning paradigms and build real-world projects related
Deep Learning with R for Beginners
Language: en
Pages: 612
Authors: Mark Hodnett
Categories: Computers
Type: BOOK - Published: 2019-05-20 - Publisher: Packt Publishing Ltd

Explore the world of neural networks by building powerful deep learning models using the R ecosystem Key FeaturesGet to grips with the fundamentals of deep lear
Deep Learning Essentials
Language: en
Pages: 284
Authors: Anurag Bhardwaj
Categories: Computers
Type: BOOK - Published: 2018-01-30 - Publisher: Packt Publishing Ltd

Get to grips with the essentials of deep learning by leveraging the power of Python Key Features Your one-stop solution to get started with the essentials of de
Deep Learning with R Cookbook
Language: en
Pages: 328
Authors: Swarna Gupta
Categories: Computers
Type: BOOK - Published: 2020-02-21 - Publisher: Packt Publishing Ltd

Tackle the complex challenges faced while building end-to-end deep learning models using modern R libraries Key FeaturesUnderstand the intricacies of R deep lea
Mastering Machine Learning with R
Language: en
Pages: 354
Authors: Cory Lesmeister
Categories: Computers
Type: BOOK - Published: 2019-01-31 - Publisher: Packt Publishing Ltd

Machine learning is a field of AI where we build systems that learn from data. This book explains complicated concepts with real-world applications. It demonstr
Deep Learning
Language: en
Pages: 158
Authors: Bhavatarini N
Categories: Computers
Type: BOOK - Published: 2022-09-09 - Publisher: MileStone Research Publications

In a very short time, deep learning has become a widely useful technique, solving and automating problems in computer vision, robotics, healthcare, physics, bio