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)

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 (; they will help you make your first steps so you are prepared to expand your knowledge. After reading the chapter, you will be able to answer questions such as the following:

  • What is an RNN?
  • Why is an RNN better than other solutions?
  • How do you train an RNN?
  • What are some problems with the RNN model?