Deep learning (DL) is an increasingly popular topic that attracts the attention of the largest corporations as well as that of all kinds of developers. Over the past five years, this field has seen massive improvements that have ultimately led us to think of DL as a highly disruptive technology with immense potential. Virtual assistants, speech recognition, and language translation are just a few examples of the direct implementation of DL techniques. Compared to image recognition or object detection, these applications use sequential data, where the nature of every result depends upon that of the previous one. For example, you can't produce a meaningful translation of a sentence from English to Spanish without tracking the words from beginning to end. For these kinds of problems, a specific type of model is being used—the recurrent neural network (RNN). In this book, we will cover the basics of RNNs and focus on some practical implementations using the popular DL library TensorFlow. All examples are accompanied by in-depth explanations of the theory to help you understand the underlying concepts behind this powerful but slightly complex model. Reading this book will leave you confident in your knowledge of RNNs and give you a good head start in using this model for your own specific use cases.
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Recurrent Neural Networks with Python Quick Start Guide
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Recurrent Neural Networks with Python Quick Start Guide
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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)
Preface
Free Chapter
Introducing Recurrent Neural Networks
Building Your First RNN with TensorFlow
Generating Your Own Book Chapter
Creating a Spanish-to-English Translator
Building Your Personal Assistant
Improving Your RNN Performance
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