Book Image

R Deep Learning Cookbook

By : PKS Prakash, Achyutuni Sri Krishna Rao
Book Image

R Deep Learning Cookbook

By: PKS Prakash, Achyutuni Sri Krishna Rao

Overview of this book

Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians. This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance. By the end of the book, you will have a logical understanding of Deep learning and different deep learning packages to have the most appropriate solutions for your problems.
Table of Contents (17 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback

Learning manifolds from autoencoders

Manifold learning is an approach in machine learning that assumes that data lies on a manifold of a much lower dimension. These manifolds can be linear or non-linear. Thus, the area tries to project the data from high-dimension space to a low dimension. For example, principle component analysis (PCA) is an example of linear manifold learning whereas an autoencoder is a non-linear dimensionality reduction (NDR) with the ability to learn non-linear manifolds in low dimensions. A comparison of linear and non-linear manifold learning is shown in the following figure:

As you can see from graph a), the data is residing at a linear manifold, whereas in graph graph b), the data is residing on a second-order non-linear manifold.

How to do it...

Let's take an output from the stacked autoencoder section and analyze how manifolds look when transferred into a different dimension.

Setting up principal component analysis

  1. Before getting into non-linear manifolds, let's analyze...