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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

TensorFlow learn

Just as Scikit-Learn is a convenient interface to traditional machine learning algorithms, tf.contrib.learn (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/learn/python/learn), formerly known as skflow, it is a simplified interface to build and train DNNs. Now it comes free with every installation of TensorFlow!

Even if you're not a fan of the syntax, it's worth looking at TensorFlow Learn as the high-level API to TensorFlow. This is because it's currently the only officially supported one. But, you should know that there are many alternative high-level APIs that may have more intuitive interfaces. If interested, refer to Keras (https://keras.io/), tf.slim (included with TF), to learn more about TensorFlow-Slim refer to https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim or TFLearn (http://tflearn.org/).

Setup

To get started with TensorFlow Learn, you only need to import it. We'll also import the estimators...

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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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