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Recurrent Neural Networks with Python Quick Start Guide

Recurrent Neural Networks with Python Quick Start Guide

By : Simeon Kostadinov
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Recurrent Neural Networks with Python Quick Start Guide

Recurrent Neural Networks with Python Quick Start Guide

3 (4)
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)
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Summary

This chapter walked you through building a fairly sophisticated neural network model using the sequence-to-sequence model implemented with the TensorFlow library.

First, you went through the theoretical part, gain an understanding of how the model works under the hood and why its application has resulted in remarkable achievements. In addition, you learned how an LSTM network works and why it is easily considered the best RNN model. 

Second, you saw how you can put the knowledge acquired here into practice using just several lines of code. In addition, you gain an understanding of how to prepare your data to fit the sequence-to-sequence model. Finally, you were able to successfully translate Spanish sentences into English. 

I really hope this chapter left you more confident in your deep learning knowledge and armed you with new skills that you can apply to future...

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