Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Principles of Data Science

You're reading from   Principles of Data Science Mathematical techniques and theory to succeed in data-driven industries

Arrow left icon
Product type Paperback
Published in Dec 2016
Publisher Packt
ISBN-13 9781785887918
Length 388 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Sinan Ozdemir Sinan Ozdemir
Author Profile Icon Sinan Ozdemir
Sinan Ozdemir
Arrow right icon
View More author details
Toc

Table of Contents (15) Chapters Close

Preface 1. How to Sound Like a Data Scientist FREE CHAPTER 2. Types of Data 3. The Five Steps of Data Science 4. Basic Mathematics 5. Impossible or Improbable – A Gentle Introduction to Probability 6. Advanced Probability 7. Basic Statistics 8. Advanced Statistics 9. Communicating Data 10. How to Tell If Your Toaster Is Learning – Machine Learning Essentials 11. Predictions Don't Grow on Trees – or Do They? 12. Beyond the Essentials 13. Case Studies Index

Case study 3 – using tensorflow

I would like to finish off our time together by looking at a somewhat more modern module that was only recently introduced by Google's machine learning division called tensorflow.

Tensorflow is an open-source machine learning module that is used primarily for its simplified deep learning and neural network abilities. I would like to take some time to introduce the module and solve a few quick problems using tensorflow. The syntax for tensorflow (like PyBrain in Chapter 12, Beyond the Essentials) is a bit different than our normal scikit-learn syntax so I will be going over it step by step. Let's start with some imports:

from sklearn import datasets, metrics
import tensorflow as tf
import numpy as np
from sklearn.cross_validation import train_test_split
%matplotlib inline

Our imports from sklearn include train_test_split, datasets, and metrics. We will be utilizing our train-test splits to reduce overfitting, we will use datasets in order to...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime