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)

Summary

This chapter reveals a full implementation of a chatbot system that manages to construct a short conversation. The prototype shows, in detail, each stage of building the intelligent chatbot. This includes collecting data, training the network, and making predictions (generating conversation). 

For the network's architecture, we use the powerful encoder-decoder sequence-to-sequence model that utilizes two recurrent neural networks, while connecting them using an encoder vector. For the actual implementation, we make use of a deep learning library built on top of TensorFlow, called TensorLayer. It simplifies most of the work by introducing simple one-line implementations of standard models such as sequence-to sequence. In addition, this library is useful for preprocessing your data before using it for training.

The next chapter shifts focus to, probably, the...