Recurrent Neural Networks with Python Quick Start Guide

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Title Page

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Contributors

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Preface

Who this book is for

What this book covers

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Download the example code files

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Conventions used

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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

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