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)

What are you going to build?

 

Your first steps into the practical world of recurrent neural networks will be to build a simple model which determines the parity (http://mathworld.wolfram.com/Parity.html) of a bit sequence . This is a warm-up exercise released by OpenAI in January 2018 (https://blog.openai.com/requests-for-research-2/). The task can be explained as follows: 

Given a binary string of a length of 50, determine whether there is an even or odd number of ones. If that number is even, output 0, otherwise 1.

Later in this chapter, we will give a detailed explanation of the solution, together with addressing the difficult parts and how to tackle them.