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Recurrent Neural Networks with Python Quick Start Guide

You're reading from  Recurrent Neural Networks with Python Quick Start Guide

Product type Book
Published in Nov 2018
Publisher Packt
ISBN-13 9781789132335
Pages 122 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Simeon Kostadinov Simeon Kostadinov
Profile icon Simeon Kostadinov
LinkedIn
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.
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Table of Contents (8) Chapters

Preface 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. Building Your Personal Assistant 6. Improving Your RNN Performance 7. Other Books You May Enjoy

Optimizing the TensorFlow library

This section focuses mostly on practical advice that can be directly implemented in your code. The TensorFlow team has provided a large set of tools that can be utilized to improve your performance. These techniques are constantly being updated to achieve better results. I strongly recommend watching TensorFlow's video on training performance from the 2018 TensorFlow conference (https://www.youtube.com/watch?v=SxOsJPaxHME). This video is accompanied by nicely aggregated documentation, which is also a must-read (https://www.tensorflow.org/performance/). 

Now, let's dive into more details around what you can do to achieve faster and more reliable training. 

Let's first start with an illustration from TensorFlow that presents the general steps of training a neural network. You can divide this process into three...

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Authors (1)

Profile icon Simeon Kostadinov
LinkedIn
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.
Read more
See other products by Simeon Kostadinov

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