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Deep Learning with TensorFlow

You're reading from   Deep Learning with TensorFlow Explore neural networks and build intelligent systems with Python

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788831109
Length 484 pages
Edition 2nd Edition
Languages
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Authors (2):
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Giancarlo Zaccone Giancarlo Zaccone
Author Profile Icon Giancarlo Zaccone
Giancarlo Zaccone
Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Deep Learning 2. A First Look at TensorFlow FREE CHAPTER 3. Feed-Forward Neural Networks with TensorFlow 4. Convolutional Neural Networks 5. Optimizing TensorFlow Autoencoders 6. Recurrent Neural Networks 7. Heterogeneous and Distributed Computing 8. Advanced TensorFlow Programming 9. Recommendation Systems Using Factorization Machines 10. Reinforcement Learning Other Books You May Enjoy Index

TFLearn


TFLearn is a library that wraps a lot of new TensorFlow APIs with the nice and familiar scikit-learn API.

TensorFlow is all about a building and executing graphs. This is a very powerful concept, but it is also cumbersome to start with.

Looking under the hood of TF.Learn, we just used three parts:

  • layers: A set of advanced TensorFlow functions that allow us to easily build complex graphs, from fully connected layers, convolution, and batch norm to losses and optimization.

  • graph_actions:  A set of tools to perform training, evaluating, and running inference on TensorFlow graphs.

  • Estimator: This packages everything into a class that follows scikit-learn interface and provides a way to easily build and train custom TensorFlow models.

Installation

To install TFLearn, the easiest way is to run the following command:

pip install git+https://github.com/tflearn/tflearn.git

For the latest stable version, use this command:

pip install tflearn

Otherwise, you can also install it from source by...

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