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Domo Arigato, Mr. Roboto: Machine Learning with Ruby
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Eric Weinstein
November 10, 2016
Technology
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1.3k
Domo Arigato, Mr. Roboto: Machine Learning with Ruby
Slides for my RubyConf 2016 talk on machine learning.
Eric Weinstein
November 10, 2016
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Transcript
Dōmo arigatō, Mr. Roboto: Machine Learning with Ruby # Eric
Weinstein # RubyConf 2016 # Cincinnati, Ohio # 10 November 2016
for Joshua
Part 0: Hello!
About Me eric_weinstein = { employer: 'Hulu', github: 'ericqweinstein', twitter:
'ericqweinstein', website: 'ericweinste.in' } 30% off with RUBYCONF30!
Agenda • What is machine learning? • What is supervised
learning? • What’s a neural network? • Machine learning with Ruby and the MNIST dataset
Part 1: Machine Learning
None
What’s machine learning?
In a word:
Generalization
What’s Supervised Learning? Classification or regression, generalizing from labeled data
to unlabeled data
Features && Labels • Raw pixel features (vectors of intensities)
• Digit (0..9)
Features && Labels • Raw pixel features (vectors of intensities)
• Digit (0..9)
Image credit: https://www.tensorflow.org/versions/r0.9/tutorials/mnist/ beginners/index.html
What’s a neural network?
Image credit: https://github.com/cdipaolo/goml/tree/master/perceptron
Image credit: https://en.wikipedia.org/wiki/Artificial_neural_network
Part 2: The MNIST Dataset
Our Data • Images of handwritten digits, size-normalized and centered
• Training: 60,000 examples, test: 10,000 • http://yann.lecun.com/exdb/mnist/
Image credit: https://www.researchgate.net/
How’d We Do? • Correct: 9328 / 10_000 • Incorrect:
672 / 10_000 • Overall: 93.28% accuracy
Developing the App
Front End submit() { fetch('/submit', { method: 'POST', body: this.state.canvas.toDataURL('image/png')
}).then(response => { return response.json(); }).then(j => { this.setState({ prediction: j.prediction }); }); }
Front End render() { return( <div> <EditableCanvas canvas={this.state.canvas} ctx={this.state.ctx} ref='editableCanvas'
/> <Prediction number={this.state.prediction} /> <div> <Button onClick={this.submit} value='Submit' /> <Button onClick={this.clear} value='Clear' /> </div> </div> ); }
Back End train = RubyFann::TrainData.new(inputs: features, desired_outputs: labels) fann =
RubyFann::Standard.new(num_inputs: 576, hidden_neurons: [300], num_outputs: 10) fann.train_on_data(train, 1000, 10, 0.01)
STOP #demotime
Summary • Machine learning is generalization • Supervised learning is
labeled data -> unlabeled data • Neural networks are awesome • You can do all this with Ruby!
Takeaways (TL;DPA) • We can do machine learning with Ruby
• Contribute to tools like Ruby FANN (github.com/tangledpath/ruby-fann) and sciruby (http://sciruby.com/) • Check it out: http://ruby-mnist.herokuapp.com/ • PRs welcome! github.com/ericqweinstein/ruby- mnist
Thank You!
Questions? eric_weinstein = { employer: 'Hulu', github: 'ericqweinstein', twitter: 'ericqweinstein',
website: 'ericweinste.in' } 30% off with RUBYCONF30!