Domo Arigato, Mr. Roboto: Machine Learning with Ruby

Slides for my RubyConf 2016 talk on machine learning.

Transcript

1. Dōmo arigatō, Mr. Roboto: Machine Learning with Ruby # Eric Weinstein # RubyConf 2016 # Cincinnati, Ohio # 10 November 2016
2. for Joshua
3. Part 0: Hello!
4. About Me eric_weinstein = { employer: 'Hulu', github: 'ericqweinstein', twitter: 'ericqweinstein', website: 'ericweinste.in' } 30% off with RUBYCONF30!
5. Agenda • What is machine learning? • What is supervised learning? • What’s a neural network? • Machine learning with Ruby and the MNIST dataset
6. Part 1: Machine Learning
8. What’s machine learning?
9. In a word:
10. Generalization
11. What’s Supervised Learning? Classification or regression, generalizing from labeled data to unlabeled data
12. Features && Labels • Raw pixel features (vectors of intensities) • Digit (0..9)
13. Features && Labels • Raw pixel features (vectors of intensities) • Digit (0..9)
14. Image credit: https://www.tensorflow.org/versions/r0.9/tutorials/mnist/ beginners/index.html
15. What’s a neural network?
16. Image credit: https://github.com/cdipaolo/goml/tree/master/perceptron
17. Image credit: https://en.wikipedia.org/wiki/Artificial_neural_network
18. Part 2: The MNIST Dataset
19. Our Data • Images of handwritten digits, size-normalized and centered • Training: 60,000 examples, test: 10,000 • http://yann.lecun.com/exdb/mnist/
20. Image credit: https://www.researchgate.net/
21. How’d We Do? • Correct: 9328 / 10_000 • Incorrect: 672 / 10_000 • Overall: 93.28% accuracy
22. Developing the App
23. 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 }); }); }
24. 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> ); }
25. 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)
26. STOP #demotime
27. Summary • Machine learning is generalization • Supervised learning is labeled data -> unlabeled data • Neural networks are awesome • You can do all this with Ruby!
28. 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
29. Thank You!
30. Questions? eric_weinstein = { employer: 'Hulu', github: 'ericqweinstein', twitter: 'ericqweinstein', website: 'ericweinste.in' } 30% off with RUBYCONF30!