#### 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.
Title Page
Credits
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Preface
Free Chapter
Getting Started
Data Representation Using Autoencoders
Recurrent Neural Networks
Reinforcement Learning
Application of Deep Learning in Text Mining
Application of Deep Learning to Signal processing

## Building an RBM model

In this recipe, we will build an RBM model as discussed (in detail) in Chapter 5, Generative Models in Deep Learning.

Let's set up our system for the model:

1. In Piano, the lowest note is 24 and the highest is 102; hence, the range of notes is 78. Thus, the number of columns in the encoded matrix is 156 (that is, 78 for note-on and 78 for note-off):
lowest_note = 24L
highest_note = 102L
note_range = highest_note-lowest_note
1. We will create notes for 15 number of steps at a time with 2,340 nodes in the input layer and 50 nodes in the hidden layer:
num_timesteps  = 15L
num_input      = 2L*note_range*num_timesteps
num_hidden       = 50L

1. The learning rate (alpha) is 0.1:
alpha<-0.1

### How to do it...

Looking into the steps of building an RBM model:

1. Define the placeholder variables:
vb <- tf\$placeholder(tf\$float32, shape = shape(num_input))
hb <- tf\$placeholder(tf\$float32, shape = shape(num_hidden))
W <- tf\$placeholder(tf\$float32, shape = shape(num_input...