Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Arrow up icon
GO TO TOP
Learning Data Mining with Python

You're reading from   Learning Data Mining with Python Harness the power of Python to analyze data and create insightful predictive models

Arrow left icon
Product type Paperback
Published in Jul 2015
Publisher Packt
ISBN-13 9781784396053
Length 344 pages
Edition 1st Edition
Languages
Tools
Concepts
Arrow right icon
Author (1):
Arrow left icon
Robert Layton Robert Layton
Author Profile Icon Robert Layton
Robert Layton
Arrow right icon
View More author details
Toc

Table of Contents (20) Chapters Close

Learning Data Mining with Python
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started with Data Mining FREE CHAPTER 2. Classifying with scikit-learn Estimators 3. Predicting Sports Winners with Decision Trees 4. Recommending Movies Using Affinity Analysis 5. Extracting Features with Transformers 6. Social Media Insight Using Naive Bayes 7. Discovering Accounts to Follow Using Graph Mining 8. Beating CAPTCHAs with Neural Networks 9. Authorship Attribution 10. Clustering News Articles 11. Classifying Objects in Images Using Deep Learning 12. Working with Big Data Next Steps… Index

Application scenario and goals


In this chapter, we will build a system that will take an image as an input and give a prediction on what the object in it is. We will take on the role of a vision system for a car, looking around at any obstacles in the way or on the side of the road. Images are of the following form:

This dataset comes from a popular dataset called CIFAR-10. It contains 60,000 images that are 32 pixels wide and 32 pixels high, with each pixel having a red-green-blue (RGB) value. The dataset is already split into training and testing, although we will not use the testing dataset until after we complete our training.

Note

The CIFAR-10 dataset is available for download at: http://www.cs.toronto.edu/~kriz/cifar.html. Download the python version, which has already been converted to NumPy arrays.

Opening a new IPython Notebook, we can see what the data looks like. First, we set up the data filenames. We will only worry about the first batch to start with, and scale up to the full dataset...

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 €14.99/month. Cancel anytime
Visually different images