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Hands-On Deep Learning with TensorFlow

You're reading from Hands-On Deep Learning with TensorFlow Uncover what is underneath your data!

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781787282773

Length 174 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Deep Learning

Author (1):

Dan Van Boxel

View More author details

Table of Contents (7) Chapters

Preface

1. Getting Started FREE CHAPTER

2. Deep Neural Networks

3. Convolutional Neural Networks

4. Introducing Recurrent Neural Networks

5. Wrapping Up

Index

Chapter 4. Introducing Recurrent Neural Networks

In the previous chapter, you learned about convolutional networks. Now, it's time to move on to a new type of model and problem—Recurrent Neural Networks (RNNs). In this chapter, we'll explain the workings of RNNs, and implement one in TensorFlow. Our example problem will be a simple season predictor with weather information. We will also take a look at skflow, a simplified interface to TensorFlow. This will let us quickly re-implement both our old image classification models and the new RNN. At the end of this chapter, you will have a good understanding of the following concepts:

Exploring RNNs
TensorFlow learn
Dense Neural Network (DNN)

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Authors (1)

Dan Van Boxel

Dan Van Boxel

Dan Van Boxel is a data scientist and machine learning engineer with over 10 years of experience. He is most well-known for Dan Does Data, a YouTube livestream demonstrating the power and pitfalls of neural networks. He has developed and applied novel statistical models of machine learning to topics such as accounting for truck traffic on highways, travel time outlier detection, and other areas. Dan has also published research articles and presented findings at the Transportation Research Board and other academic journals.

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