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Large Scale Machine Learning with Python

You're reading from   Large Scale Machine Learning with Python Learn to build powerful machine learning models quickly and deploy large-scale predictive applications

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Product type Paperback
Published in Aug 2016
Publisher Packt
ISBN-13 9781785887215
Length 420 pages
Edition 1st Edition
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Authors (3):
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Alberto Boschetti Alberto Boschetti
Author Profile Icon Alberto Boschetti
Alberto Boschetti
Bastiaan Sjardin Bastiaan Sjardin
Author Profile Icon Bastiaan Sjardin
Bastiaan Sjardin
Luca Massaron Luca Massaron
Author Profile Icon Luca Massaron
Luca Massaron
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Toc

Table of Contents (12) Chapters Close

Preface 1. First Steps to Scalability FREE CHAPTER 2. Scalable Learning in Scikit-learn 3. Fast SVM Implementations 4. Neural Networks and Deep Learning 5. Deep Learning with TensorFlow 6. Classification and Regression Trees at Scale 7. Unsupervised Learning at Scale 8. Distributed Environments – Hadoop and Spark 9. Practical Machine Learning with Spark A. Introduction to GPUs and Theano Index

TensorFlow installation

The version of TensorFlow that we will use in this chapter is 0.8 so make sure that you install this version. As TensorFlow is in heavy development, small changes are due. We can install TensorFlow quite easily with pip install, independent of which operating system you use:

pip install tensorflow

If you already have previous versions installed, you can upgrade according to your operating system:

# Ubuntu/Linux 64-bit, CPU only:
$ sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.1-cp27-none-linux_x86_64.whl

# Ubuntu/Linux 64-bit, GPU enabled:
$ sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.8.1-cp27-none-linux_x86_64.whl

# Mac OS X, CPU only:
$ sudo easy_install --upgrade six
$ sudo pip install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.8.1-cp27-none-any.whl

Now that TensorFlow is installed, you can test it in the terminal:

$python
import tensorflow as...
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