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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

Installing Theano

First, make sure that you install the development version from the Theano page. Note that if you do "$ pip install theano", you might end up with problems. Installing the development version from GitHub directly is a safer bet:

$ git clone git://github.com/Theano/Theano.git
$  pip install Theano

If you want to upgrade Theano, you can use the following command:

$ sudo pip install --upgrade theano

If you have questions and want to connect with the Theano community, you can refer to https://groups.google.com/forum/#!forum/theano-users.

That's it, we are ready to go!

To make sure that we set the directory path toward the Theano folder, we need to do the following:

#!/usr/bin/python
import cPickle as pickle
from six.moves import cPickle as pickle
import os

#set your path to the theano folder here
path = '/Users/Quandbee1/Desktop/pthw/Theano/'

Let's install all the packages that we need:

from theano import tensor
import theano.tensor as T
import theano...
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