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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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Summary

 

In this chapter, you went through the process of building a book chapter generator using a Gated Recurrent Unit neural network. You understood what sits behind this powerful model and how you can put it into practice with a handful of lines of code using TensorFlow. In addition, you faced the challenge of preparing and clearing your data so that your model is trained correctly. 

In the next chapter, you will fortify your skills by implementing your first real-life practical application—a language translator. You have probably faced the online Google Translate software and were amazed by how well it worked. In the next chapter, you will understand what sits behind a sophisticated system like that and why its level of accuracy has increased drastically in recent years. 

I hope the current chapter advanced your deep learning knowledge and that you...

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