Recurrent Neural Networks with Python Quick Start Guide: Sequential learning and language modeling with TensorFlow

by Simeon Kostadinov

Overview

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.

Product Details

ISBN-13:	9781789133660
Publisher:	Packt Publishing
Publication date:	11/30/2018
Sold by:	Barnes & Noble
Format:	eBook
Pages:	122
File size:	5 MB

About the Author

Simeon Kostadinov is a student at the University of Birmingham, who also lives in San Francisco and works for a startup called Speechify which aims to help people go through their readings faster by converting any text into speech. Simeon is Machine Learning enthusiast who writes a blog and works on various projects on the side. His technical experience includes heavy university knowledge, two summer internships and two years of practical experience. Moreover, his blog includes explanations of numerous deep learning techniques. He enjoys reading different research papers and implement some of them in code. His interest covers both the theoretical as well as practical side of deep learning since his background is in mathematics and throughout time he ignited his interest in computer science. He was ranked number 1 in mathematics during his senior year of high school and thus he has deep passion about understanding how the deep learning models work under the hood. His specific knowledge in Recurrent Neural Networks comes from several courses that he has taken at Stanford University and University of Birmingham. They helped in understanding how to apply his theoretical knowledge into practice and build powerful models. In addition, he recently became a Stanford Scholar Initiative which includes working in a team of Machine Learning researchers on a specific deep learning research paper.
Simeon Kostadinoff works for a startup called Speechify which aims to help people go through their readings faster by converting any text into speech. Simeon is Machine Learning enthusiast who writes a blog and works on various projects on the side. He enjoys reading different research papers and implement some of them in code. He was ranked number 1 in mathematics during his senior year of high school and thus he has deep passion about understanding how the deep learning models work under the hood. His specific knowledge in Recurrent Neural Networks comes from several courses that he has taken at Stanford University and University of Birmingham. They helped in understanding how to apply his theoretical knowledge into practice and build powerful models. In addition, he recently became a Stanford Scholar Initiative which includes working in a team of Machine Learning researchers on a specific deep learning research paper.

Table of Contents

Table of Contents

1. Introducing Recurrent Neural Networks
2. Building Your First RNN with TensorFlow
3. Generating Your Own Book Chapter
4. Creating a Spanish-to-English Translator
5. Build Your Personal Assistant
6. Improve Your RNN Performance