Book Image

Recurrent Neural Networks with Python Quick Start Guide

By : Simeon Kostadinov
Book Image

Recurrent Neural Networks with Python Quick Start Guide

By: Simeon Kostadinov

Overview of this book

Developers struggle to find an easy-to-follow learning resource for implementing Recurrent Neural Network (RNN) models. RNNs are the state-of-the-art model in deep learning for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously improve results. This book will teach you the fundamentals of RNNs, with example applications in Python and the TensorFlow library. The examples are accompanied by the right combination of theoretical knowledge and real-world implementations of concepts to build a solid foundation of neural network modeling. Your journey starts with the simplest RNN model, where you can grasp the fundamentals. The book then builds on this by proposing more advanced and complex algorithms. We use them to explain how a typical state-of-the-art RNN model works. From generating text to building a language translator, we show how some of today's most powerful AI applications work under the hood. After reading the book, you will be confident with the fundamentals of RNNs, and be ready to pursue further study, along with developing skills in this exciting field.
Table of Contents (8 chapters)

Coding the recurrent neural network

As mentioned before, the aim of our task is to build a recurrent neural network that predicts the parity of a bit sequence. We will approach this problem in a slightly different way. Since the parity of a sequence depends on the number of ones, we will sum up the elements of the sequence and find whether the result is even or not. If it is even, we will output 0, otherwise, 1

This section of the chapter includes code samples and goes through the following steps:

  • Generating data to train the model
  • Building the TensorFlow graph (using TensorFlow's built-in functions for recurrent neural networks)
  • Training the neural network with the generated data
  • Evaluating the model and determining its accuracy

Generating data

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