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Rubyconf Neural Networks

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Neural networks are an excellent way of mapping past observations to a functional model. Many researchers have been able to build tools to recognize handwriting, or even jaundice detection. While Neural Networks are powerful they still are somewhat of a mystery to many. This talk aims to explain neural networks in a test driven way. We'll write tests first and go through how to build a neural network to determine what language a sentence is. By the end of this talk you'll know how to build neural networks with tests!

Technology
1 of 38
Test Driven Neural Networks Matthew Kirk - Modulus 7
automatic playlists
The Challenge
“Big Data” Languages Java Python R Julia Clojure
Ruby has tools too
Ruby is not Complex Math
Today we’ll cover • • • • What feed forward neural networks are How to classify strings to languages using Neural Nets How to do it in a TDD fashion Demonstration
Neural Networks! aka the sledge hammer of functional relationships
Neural Networks
Input layer
Hidden layer
How many Neurons? • 2/3 * Input layer count + output count is a good start • Aggregation over expansion so less neurons in the hidden layer than on the input layer.
Output layer
Neurons
Digital Logic
Fuzzy Logic
Activation Functions • Sigmoid • Elliott • Gaussian • Linear • Threshold • Cosine and Sine
Activation Functions • Sigmoid => Learning Curve • Elliott => Learning Curve • Gaussian => Bell curve • Linear => Line • Threshold => Yes or No • Cosine and Sine => Periodic
Training Algorithms • Quickprop • RProp => Use this • Back propagation
Visually What they do
Just the tip of the Neural Nets iceberg
Specifically • English • German • Polish • Swedish • Finnish • Norwegian
Data Collection • Using the most translated book in the world “The Bible” to collect sentences used in each of these languages.
Now What?
Character Distribution
TDD Neural Nets
Test the Seams describe Language do it 'has the proper keys for each vector' it 'sums to 1 for all vectors' it 'returns characters that is a unique set of characters used' end
Cross Validation describe Network do %w[English Finnish German Norwegian Polish Swedish].each do |lang| it "Trains and cross-validates with an error of 5% for #{lang}" end end
Ockham’s Razor
Demo
modulus7.com/rubyconf @mjkirk
Conclusion This is just the beginning Go learn more become more adept at data analysis
Photo Credits http://rickmanelius.com/article/do-you-dread-emails http://www.flickr.com/photos/irisheyes/8469160004/ http://www.flickr.com/photos/andy_bernay-roman/2206610268/ http://www.flickr.com/photos/kev_walsh/2216144544/sizes/o/in/photostream/ http://www.flickr.com/photos/clover_1/2926385130/ http://www.flickr.com/photos/andy_bernay-roman/2206610268/sizes/o/in/photostream/ http://translate.google.com http://www.flickr.com/photos/epistemographer/68200471 http://www.allaboutcircuits.com/vol_4/chpt_3/5.html http://www.flickr.com/photos/brunobord/3987593006/

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