Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
R Deep Learning Cookbook

You're reading from   R Deep Learning Cookbook Solve complex neural net problems with TensorFlow, H2O and MXNet

Arrow left icon
Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787121089
Length 288 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (2):
Arrow left icon
Achyutuni Sri Krishna Rao Achyutuni Sri Krishna Rao
Author Profile Icon Achyutuni Sri Krishna Rao
Achyutuni Sri Krishna Rao
PKS Prakash PKS Prakash
Author Profile Icon PKS Prakash
PKS Prakash
Arrow right icon
View More author details
Toc

Table of Contents (11) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Deep Learning with R 3. Convolution Neural Network 4. Data Representation Using Autoencoders 5. Generative Models in Deep Learning 6. Recurrent Neural Networks 7. Reinforcement Learning 8. Application of Deep Learning in Text Mining 9. Application of Deep Learning to Signal processing 10. Transfer Learning

Training a Restricted Boltzmann machine


Every training step of an RBM goes through two phases: the forward phase and the backward phase (or reconstruction phase). The reconstruction of visible units is fine tuned by making several iterations of the forward and backward phases.

Training a forward phase: In the forward phase, the input data is passed from the visible layer to the hidden layer and all the computation occurs within the nodes of the hidden layer. The computation is essentially to take a stochastic decision of each connection from the visible to the hidden layer. In the hidden layer, the input data (X) is multiplied by the weight matrix (W) and added to a hidden bias vector (hb).

The resultant vector of a size equal to the number of hidden nodes is then passed through a sigmoid function to determine each hidden node's output (or activation state). In our case, each input digit will produce a tensor vector of 900 probabilities, and as we have 55,000 input digits, we will have an...

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
Banner background image