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- Herausgeber: Packt Publishing
- Kategorie: Fachliteratur
- Sprache: Englisch
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.
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Seitenzahl: 136
Veröffentlichungsjahr: 2018
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Recurrent Neural Networks with Python Quick Start Guide
Copyright © 2018 Packt Publishing
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First published: November 2018
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ISBN 978-1-78913-233-5
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Contributors
About the author
Simeon Kostadinov is a software engineer and deep learning enthusiast who loves learning while being involved in long-term projects that aim to improve peoples' lives. He is currently in his final year at the University of Birmingham studying computer science. During his studies, he spent a year in San Francisco working with the incredibly smart Speechify team. His programming knowledge includes Swift, Python, and JavaScript. Simeon is also passionate about the field of AI and how it can transform the businesses of today.
About the reviewer
Boyan Bonev is a software engineer and machine learning enthusiast with experience in web and desktop development. He has been an intern for both IBM and CERN and so is highly skilled in C#, JavaScript, Java, Python, Haskell, and various frameworks. He has also been involved in working with teams using different agile techniques. He is very passionate about cognitive technologies that will change the future, which is why he has taken so many Coursera courses. Outside his hobby, he loves spending time with his family and friends. Boyan is also very passionate about helping young adults who have been diagnosed with leukemia.
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Table of Contents
Title Page
Copyright and Credits
Recurrent Neural Networks with Python Quick Start Guide
About Packt
Why subscribe?
Packt.com
Contributors
About the author
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Introducing Recurrent Neural Networks
What is an RNN?
Comparing recurrent neural networks with similar models
Hidden Markov model
Recurrent neural network
Understanding how recurrent neural networks work
Basic neural network overview
Obtaining data
Encoding the data
Building the architecture 
Training the model
Evaluating the model
Key problems with the standard recurrent neural network model
Summary
External links
Building Your First RNN with TensorFlow
What are you going to build?
Introduction to TensorFlow
Graph-based execution
Eager execution
Coding the recurrent neural network
Generating data
Building the TensorFlow graph
Training the RNN
Evaluating the predictions
Summary
External links
Generating Your Own Book Chapter
Why use the GRU network?
Generating your book chapter
Obtaining the book text
Encoding the text
Building the TensorFlow graph
Training the network
Generating your new text
Summary
External links
Creating a Spanish-to-English Translator
Understanding the translation model
What is an LSTM network?
Understanding the sequence-to-sequence network with attention
Building the Spanish-to-English translator
Preparing the data
Constructing the TensorFlow graph
Training the model
Predicting the translation
Evaluating the final results
Summary
External links
Building Your Personal Assistant
What are we building?
Preparing the data
Creating the chatbot network
Training the chatbot
Building a conversation
Summary
External links
Improving Your RNN Performance
Improving your RNN model
Improving performance with data
Selecting data
Processing data
Transforming data
Improving performance with tuning
Grid search
Random search 
Hand-tuning
Bayesian optimization
Tree-structured Parzen Estimators (TPE)
Optimizing the TensorFlow library
Data processing
Improving data loading
Improving data transformation
Performing the training
Optimizing gradients
Summary
External links
Other Books You May Enjoy
Leave a review - let other readers know what you think
Preface
Deep learning (DL) is an increasingly popular topic that attracts the attention of the largest corporations as well as that of all kinds of developers. Over the past five years, this field has seen massive improvements that have ultimately led us to think of DL as a highly disruptive technology with immense potential. Virtual assistants, speech recognition, and language translation are just a few examples of the direct implementation of DL techniques. Compared to image recognition or object detection, these applications use sequential data, where the nature of every result depends upon that of the previous one. For example, you can't produce a meaningful translation of a sentence from English to Spanish without tracking the words from beginning to end. For these kinds of problems, a specific type of model is being used—the recurrent neural network (RNN). In this book, we will cover the basics of RNNs and focus on some practical implementations using the popular DL library TensorFlow. All examples are accompanied by in-depth explanations of the theory to help you understand the underlying concepts behind this powerful but slightly complex model. Reading this book will leave you confident in your knowledge of RNNs and give you a good head start in using this model for your own specific use cases.
Who this book is for
This book is for machine learning engineers and data scientists who want to learn about RNNs by looking at practical use cases.
What this book covers
Chapter 1, Introducing Recurrent Neural Networks, will provide you with a brief introduction to the basics of RNNs and will compare the model to other popular models and demonstrate why RNNs are the best. This chapter will then illustrate RNNs with the use of an example. You will also be given insight into the problems that RNNs have.
Chapter 2, Building Your First RNN with TensorFlow, will explore how to build a simple RNN to solve the problem of identifying sequence parity. You will also gain a brief understanding of the TensorFlow library and how it can be utilized for building DL models. After reading this chapter, you should have a full understanding of how to use TensorFlow with Python and how easy and straightforward it is to build a neural network.
Chapter 3, Generating Your Own Book Chapter, will also introduce a new and more powerful RNN model called the gated recurrent unit (GRU). You will learn how it works and why we are choosing it over the simple RNN. You will also go step by step over the process of generating a book chapter. By the end of this chapter, you should have gained both a theoretical and a practical knowledge that will give you the freedom to experiment with any problems of medium difficulty.
Chapter 4, Creating a Spanish-to-English Translator, will walk you through building a fairly sophisticated neural network model using the sequence-to-sequence model implemented with the TensorFlow library. You will build a simple version of a Spanish-to-English translator, which will accept a sentence in Spanish and output its English equivalent.
Chapter 5, Building Your Personal Assistant, will then look on the practical side of RNNs and have you build a conversational chatbot. This chapter reveals a full implementation of a chatbot system that manages to construct a short conversation. You will then create an end-to-end model that aims to yield meaningful results. You will make use of a high-level TensorFlow-based library called TensorLayer.
Chapter 6, Improving Your RNN's Performance, will go through some techniques for improving your RNN. This chapter will focus on improving your RNN's performance with data and tuning. You will also look into optimizing the TensorFlow library for better results.
To get the most out of this book
You need a basic knowledge of Python 3.6.x and basic knowledge of Linux commands. Previous experience with TensorFlow would be helpful, but is not mandatory.
Download the example code files
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Download the color images
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://www.packtpub.com/sites/default/files/downloads/9781789132335_ColorImages.pdf.
Get in touch
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Introducing Recurrent Neural Networks
This chapter will introduce you to the theoretical side of the recurrent neural network (RNN) model. Gaining knowledge about what lies behind this powerful architecture will give you a head start on mastering the practical examples that are provided later in the book. Since you may often find yourself in a situation where a critical decision for your application is needed, it is essential to be aware of the building parts of this model. This will help you act appropriately for the situation.
The prerequisite knowledge for this chapter includes basic linear algebra (matrix operations). A basic knowledge in deep learning and neural networks is also a plus. If you are new to that field, I would recommend first watching the great series of videos made by Andrew Ng (https://www.youtube.com/playlist?list=PLkDaE6sCZn6Ec-XTbcX1uRg2_u4xOEky0
