Book Image

Deep Learning with R for Beginners

By : Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado
Book Image

Deep Learning with R for Beginners

By: Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado

Overview of this book

Deep learning has a range of practical applications in several domains, while R is the preferred language for designing and deploying deep learning models. This Learning Path introduces you to the basics of deep learning and even teaches you to build a neural network model from scratch. As you make your way through the chapters, you’ll explore deep learning libraries and understand how to create deep learning models for a variety of challenges, right from anomaly detection to recommendation systems. The Learning Path will then help you cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud, in addition to model optimization, overfitting, and data augmentation. Through real-world projects, you’ll also get up to speed with training convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in R. By the end of this Learning Path, you’ll be well-versed with deep learning and have the skills you need to implement a number of deep learning concepts in your research work or projects.
Table of Contents (23 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

What is so exciting about recurrent neural networks?


Coming from a mathematics background, in my rather hectic career I have seen many different trends, particularly during the last few years, which all sound very similar to me: "you have a problem? wavelets can save you!", "finite elements are the solution to everything", and similar over-enthusiastic claims. 

Of course, each tool has its time and place and, more importantly, an application domain where it excels. I find recurrent neural networks quite interesting for the many features they can achieve:

  • Produce consistent markup text (opening and closing tags, recognizing timestamp-like data)
  • Write Wikipedia articles with references, and create URLs from non-existing addresses, by learning what a URL should look like
  • Create credible-looking scientific papers from LaTeX

All these amazing features are possible without the network having any context information or metadata. In particular, without knowing English, nor what a URL or a bit of LaTeX...