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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 explored how to build a simple recurrent neural network to solve the problem of identifying sequence parity. You obtained a brief understanding of the TensorFlow library and how it can be utilized for building deep learning models. I hope the study of this chapter leaves you more confident in your deep learning knowledge, as well as excited to learn and grow more in this field. 

In the next chapter, you will go a step further by implementing a more sophisticated neural network for the task of generating text. You will gain both theoretical and practical experience. This will result in you learning about a new type of network, GRU, and understanding how to implement it in TensorFlow. In addition, you will face the challenge of formatting your input text correctly as well as using it for training the TensorFlow graph. 

I can assure you that...

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