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Recurrent Neural Networks with Python Quick Start Guide

You're reading from Recurrent Neural Networks with Python Quick Start Guide Sequential learning and language modeling with TensorFlow

Product type Paperback

Published in Nov 2018

Publisher Packt

ISBN-13 9781789132335

Length 122 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Neural Networks

Author (1):

Simeon Kostadinov

View More author details

Table of Contents (8) Chapters

Preface

1. Introducing Recurrent Neural Networks FREE CHAPTER

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

Leave a review - let other readers know what you think

External links

Parity: http://mathworld.wolfram.com/Parity.html
Request for Research 2.0 by OpenAI: https://blog.openai.com/requests-for-research-2/
Eager execution documentation: https://www.tensorflow.org/guide/eager
Eager execution (TensorFlow Conference 2018): https://www.youtube.com/watch?v=T8AW0fKP0Hs
Python installation: https://www.python.org/downloads/
Understanding num_hidden_units : https://stackoverflow.com/questions/37901047/what-is-num-units-in-tensorflow-basiclstmcell
Adam versus Gradient descent optimizer: https://stats.stackexchange.com/questions/184448/difference-between-gradientdescentoptimizer-and-adamoptimizer-tensorflow
Understanding sess.run(tf.global_variables_initializer(): https://stackoverflow.com/questions/44433438/understanding-tf-global-variables-initializer

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Authors (1)

Kostadinov

Kostadinov

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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