Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
R Deep Learning Projects

You're reading from   R Deep Learning Projects Master the techniques to design and develop neural network models in R

Arrow left icon
Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781788478403
Length 258 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (2):
Arrow left icon
Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
Pablo Maldonado Pablo Maldonado
Author Profile Icon Pablo Maldonado
Pablo Maldonado
Arrow right icon
View More author details
Toc

RNN using Keras

In this section, we introduce an example using Keras. Keras is possibly the highest-level API for deep learning (again, at the time of writing, in this rapidly changing world of deep learning). This is very useful when you need to do production-ready models quite quickly, but is unfortunately sometimes not that great for learning, as everything is hidden away from you. Since, ideally, by the time you reach this section, an expert in recurrent neural networks, we can present you how to create a similar model. 

Before that, let's introduce a simple benchmark model. Something that comes to mind when we speak about the memory of a neural network is the following, well, what if I had sufficient storage to calculate the conditional probabilities and simulate text generation as a Markov process, where the state variable is the observed text? We will implement...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime