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Recurrent Neural Networks with Python Quick Start Guide
Recurrent Neural Networks with Python Quick Start Guide

Recurrent Neural Networks with Python Quick Start Guide: Sequential learning and language modeling with TensorFlow

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Recurrent Neural Networks with Python Quick Start Guide

Building Your First RNN with TensorFlow

In this chapter, you will gain a hands-on experience of building a recurrent neural network (RNN). First, you will be introduced to the most widely used machine learning library—TensorFlow. From learning the basics to advancing into some fundamental techniques, you will obtain a reasonable understanding of how to apply this powerful library to your applications. Then, you will take on a fairly simple task of building an actual model. The process will show you how to prepare your data, train the network, and make predictions.

In summary, the topics of this chapter include the following:

  • What are you going to build?: Introduction of your task
  • Introduction to TensorFlow: Taking first steps into learning the TensorFlow framework
  • Coding the RNN: You will go through the process of writing your first neural network using TensorFlow. This...

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.

Introduction to TensorFlow

TensorFlow is an open source library built by Google, which aims to assist developers in creating machine learning models of any kind. The recent improvements in the deep learning space created the need for an easy and fast way of building neural networks. TensorFlow addresses this problem in an excellent fashion, by providing a wide range of APIs and tools to help developers focus on their specific problem, rather than dealing with mathematical equations and scalability issues. 

TensorFlow offers two main ways of programming a model:

  • Graph-based execution
  • Eager execution

Graph-based execution

Graph-based execution is an alternative way of representing mathematical equations and functions...

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

...

Summary

In this chapter, you explored how to build a simple recurrent neural network to solve the problem of identifying sequence parity. You obtained a brief understanding of the TensorFlow library and how it can be utilized for building deep learning models. I hope the study of this chapter leaves you more confident in your deep learning knowledge, as well as excited to learn and grow more in this field. 

In the next chapter, you will go a step further by implementing a more sophisticated neural network for the task of generating text. You will gain both theoretical and practical experience. This will result in you learning about a new type of network, GRU, and understanding how to implement it in TensorFlow. In addition, you will face the challenge of formatting your input text correctly as well as using it for training the TensorFlow graph. 

I can assure you that...

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Key benefits

  • Train and deploy Recurrent Neural Networks using the popular TensorFlow library
  • Apply long short-term memory units
  • Expand your skills in complex neural network and deep learning topics

Description

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.

Who is this book for?

This book is for Machine Learning engineers and data scientists who want to learn about Recurrent Neural Network models with practical use-cases. Exposure to Python programming is required. Previous experience with TensorFlow will be helpful, but not mandatory.

What you will learn

  • Use TensorFlow to build RNN models
  • Use the correct RNN architecture for a particular machine learning task
  • Collect and clear the training data for your models
  • Use the correct Python libraries for any task during the building phase of your model
  • Optimize your model for higher accuracy
  • Identify the differences between multiple models and how you can substitute them
  • Learn the core deep learning fundamentals applicable to any machine learning model

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Nov 30, 2018
Length: 122 pages
Edition : 1st
Language : English
ISBN-13 : 9781789132335
Vendor :
Google
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Languages :
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Product Details

Publication date : Nov 30, 2018
Length: 122 pages
Edition : 1st
Language : English
ISBN-13 : 9781789132335
Vendor :
Google
Category :
Languages :
Tools :

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Table of Contents

7 Chapters
Introducing Recurrent Neural Networks Chevron down icon Chevron up icon
Building Your First RNN with TensorFlow Chevron down icon Chevron up icon
Generating Your Own Book Chapter Chevron down icon Chevron up icon
Creating a Spanish-to-English Translator Chevron down icon Chevron up icon
Building Your Personal Assistant Chevron down icon Chevron up icon
Improving Your RNN Performance Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
(4 Ratings)
5 star 25%
4 star 25%
3 star 0%
2 star 25%
1 star 25%
Amazon Customer Nov 11, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Worth the time and worth the read. Excellent writing. I learned a lot about exactly what I was looking for.Cliff
Amazon Verified review Amazon
PC Jun 29, 2020
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Could go more into detail and cite papers.
Amazon Verified review Amazon
CustomerAmz Dec 10, 2019
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
I was expecting more on the topic, the content covered can be easily found in any book on deep learning.Examples can also be found in general deep learning book.This book had to provide much in depth into the topic.Some references are given so I am giving one extra star to the book.
Amazon Verified review Amazon
Tito Apr 19, 2020
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Code and operations in this book is no longer supported. There is no errata or update for that in the website of packt. The other issue is that there are critical diagrams missing in the book. It says referring to the above diagram but there are none. If you want to take steps backward in your learning then go ahead buy this book.
Amazon Verified review Amazon
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