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 Use Python to manipulate data and build predictive models

Arrow left icon
Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787126787
Length 358 pages
Edition 2nd Edition
Languages
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

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
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. Features and scikit-learn Transformers 6. Social Media Insight using Naive Bayes 7. Follow Recommendations Using Graph Mining 8. Beating CAPTCHAs with Neural Networks 9. Authorship Attribution 10. Clustering News Articles 11. Object Detection in Images using Deep Neural Networks 12. Working with Big Data 13. Next Steps...

Artificial neural networks


Neural networks are a class of algorithm that was originally designed based on the way that human brains work. However, modern advances are generally based on mathematics rather than biological insights. A neural network is a collection of neurons that are connected together. Each neuron is a simple function of its inputs, which are combined using some function to generate an output:

The functions that define a neuron's processing can be any standard function, such as a linear combination of the inputs, and is called the activation function. For the commonly used learning algorithms to work, we need the activation function to be derivable and  smooth. A frequently used activation function is the logistic function, which is defined by the following equation (k is often simply 1, x is the inputs into the neuron, and L is normally 1, that is, the maximum value of the function):

The value of this graph, from -6 to +6, is shown below. The red lines indicate that the value...

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 $15.99/month. Cancel anytime
Visually different images