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.

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Table of Contents (8 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Introducing Recurrent Neural Networks
Introducing Recurrent Neural Networks
What is an RNN?
Comparing recurrent neural networks with similar models
Understanding how recurrent neural networks work
Key problems with the standard recurrent neural network model
Summary
External links
2
Building Your First RNN with TensorFlow
Building Your First RNN with TensorFlow
What are you going to build?
Introduction to TensorFlow
Coding the recurrent neural network
Summary
External links
3
Generating Your Own Book Chapter
Generating Your Own Book Chapter
Why use the GRU network?
Generating your book chapter
Summary
External links
4
Creating a Spanish-to-English Translator
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
Summary
External links
5
Building Your Personal Assistant
Building Your Personal Assistant
What are we building?
Preparing the data
Creating the chatbot network
Training the chatbot
Building a conversation
Summary
External links
6
Improving Your RNN Performance
Improving Your RNN Performance
Improving your RNN model
Optimizing the TensorFlow library
Summary
External links
7
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Optimizing the TensorFlow library

This section focuses mostly on practical advice that can be directly implemented in your code. The TensorFlow team has provided a large set of tools that can be utilized to improve your performance. These techniques are constantly being updated to achieve better results. I strongly recommend watching TensorFlow's video on training performance from the 2018 TensorFlow conference (https://www.youtube.com/watch?v=SxOsJPaxHME). This video is accompanied by nicely aggregated documentation, which is also a must-read (https://www.tensorflow.org/performance/). 

Now, let's dive into more details around what you can do to achieve faster and more reliable training. 

Let's first start with an illustration from TensorFlow that presents the general steps of training a neural network. You can divide this process into three...

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