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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

2. Deep Neural Networks FREE CHAPTER

3. Convolutional Neural Networks

4. Introducing Recurrent Neural Networks

5. Wrapping Up

Index

Chapter 5. Wrapping Up

In the previous chapter, we learned about another interface to TensorFlow and RNN models. This chapter will wrap up our look at TensorFlow, looking at how far we've come and where you can go from here. First, we'll review our progress on the font classification problem, then we'll briefly look at TensorFlow beyond deep learning and see where it will go in the future. At the end of the chapter, you will be familiar with the following concepts:

Research evaluation
A quick review of all the models
The future of TensorFlow
Some more TensorFlow projects

Let's now begin by looking at research evaluation in detail.

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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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