Editeur: Packt Publishing
Build automatic classification and prediction models using unsupervised learning
About 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 prediction
Who This Book Is For
This 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 data
Deep 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 approach
This 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.
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