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
About the Authors
About the Reviewer
Customer Feedback

Bidirectional RNNs

In this section, we will look at a new neural network topology that is gaining momentum in the area of NLP.

Schuster and Paliwal have introduced Bidirectional Recurrent Neural Networks (BRNN) in 1997. BRNNs help increase the amount of input information available to the network. Multilayer perceptrons (MLPs) and time delay neural networks (TDNNs) are known to have limitations on the input data flexibility. RNNs also require their input data to be fixed. More advanced topologies like RNNs also have restrictions as the future input information cannot be predicted from the current state. BRNNs, on the contrary, do not need their input data to be fixed. Their future input information is reachable from the current state. The idea of BRNNs is to connect two hidden layers of opposite directions to the same output. With this structure, the output layer is able to get information from past and future states.

BRNNs are useful when the context of the input is needed. As an example,...