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Recurrent Neural Networks with Python Quick Start Guide

Recurrent Neural Networks with Python Quick Start Guide

By : Kostadinov
3 (4)
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Recurrent Neural Networks with Python Quick Start Guide

Recurrent Neural Networks with Python Quick Start Guide

3 (4)
By: 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)
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Improving Your RNN Performance

This chapter goes through some techniques for improving your recurrent neural network model. Often, the initial results from your model can be disappointing, so you need to find ways of improving them. This can be done with various methods and tools, but we will focus on two main areas:

  • Improving the RNN model performance with data and tuning
  • Optimizing the TensorFlow library for better results

First, we will see how more data, as well as tuning the hyperparameters, can yield significantly better results. Then our focus will shift to getting the most out of the built-in TensorFlow functionality. Both approaches are applicable to any task that involves the neural network model, so the next time you want to do image recognition with convolutional networks or fix a rescaled image with GAN, you can apply the same techniques for perfecting your model...

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