Book Image

Neural Network Programming with TensorFlow

By : Manpreet Singh Ghotra, Rajdeep Dua
Book Image

Neural Network Programming with TensorFlow

By: Manpreet Singh Ghotra, Rajdeep Dua

Overview of this book

If you're aware of the buzz surrounding the terms such as "machine learning," "artificial intelligence," or "deep learning," you might know what neural networks are. Ever wondered how they help in solving complex computational problem efficiently, or how to train efficient neural networks? This book will teach you just that. You will start by getting a quick overview of the popular TensorFlow library and how it is used to train different neural networks. You will get a thorough understanding of the fundamentals and basic math for neural networks and why TensorFlow is a popular choice Then, you will proceed to implement a simple feed forward neural network. Next you will master optimization techniques and algorithms for neural networks using TensorFlow. Further, you will learn to implement some more complex types of neural networks such as convolutional neural networks, recurrent neural networks, and Deep Belief Networks. In the course of the book, you will be working on real-world datasets to get a hands-on understanding of neural network programming. You will also get to train generative models and will learn the applications of autoencoders. By the end of this book, you will have a fair understanding of how you can leverage the power of TensorFlow to train neural networks of varying complexities, without any hassle. While you are learning about various neural network implementations you will learn the underlying mathematics and linear algebra and how they map to the appropriate TensorFlow constructs.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Chapter 7. Deep Belief Networking

A deep belief network (DBN) is a class of deep neural network, composed of multiple layers of hidden units, with connections between the layers; where a DBN differs is these hidden units don't interact with other units within each layer. A DBN can learn to probabilistically reconstruct its input without supervision, when trained, using a set of training datasets. It is a joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more.

The layers act as feature detectors. After the training step, a DBN can be trained with supervision to perform classification.

We will be covering the following chapters in the chapter:

  • Understanding deep belief networks
  • Model training
  • Predicting the label
  • Finding the accuracy of the model
  • DBN implementation for the MNIST...