Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Domo Arigato, Mr. Roboto: Machine Learning with...
Search
Eric Weinstein
November 10, 2016
Technology
1
1.5k
Domo Arigato, Mr. Roboto: Machine Learning with Ruby
Slides for my RubyConf 2016 talk on machine learning.
Eric Weinstein
November 10, 2016
Tweet
Share
More Decks by Eric Weinstein
See All by Eric Weinstein
Interview Them Where They Are
ericqweinstein
0
110
Value Your Types!
ericqweinstein
0
86
Being Good: An Introduction to Robo- and Machine Ethics
ericqweinstein
1
1.8k
What If...?: Ruby 3
ericqweinstein
1
200
Infinite State Machine
ericqweinstein
1
120
Do Androids Dream of Electronic Dance Music?
ericqweinstein
1
98
Machine Learning with Elixir and Phoenix
ericqweinstein
1
940
Machine Learning with Clojure and Apache Spark
ericqweinstein
1
400
A Nil Device, A Lonely Operator, and a Voyage to the Void Star
ericqweinstein
1
970
Other Decks in Technology
See All in Technology
ClaudeCode_vs_GeminiCLI_Terraformで比較してみた
tkikuchi
1
4.3k
機械学習を「社会実装」するということ 2025年夏版 / Social Implementation of Machine Learning July 2025 Version
moepy_stats
1
330
An introduction to Claude Code SDK
choplin
3
3.1k
低レイヤソフトウェア技術者が YouTuberとして食っていこうとした話
sat
PRO
7
5.8k
Amazon SNSサブスクリプションの誤解除を防ぐ
y_sakata
3
200
How Do I Contact Jetblue Airlines® Reservation Number: Fast Support Guide
thejetblueairhelpsupport
0
250
Figma Dev Mode MCP Serverを用いたUI開発
zoothezoo
1
290
ML Pipelineの開発と運用を OpenTelemetryで繋ぐ @ OpenTelemetry Meetup 2025-07
getty708
0
150
P2P通信の標準化 WebRTCを知ろう
faithandbrave
6
2k
All About Sansan – for New Global Engineers
sansan33
PRO
1
1.2k
分散トレーシングによる コネクティッドカーのデータ処理見える化の試み
thatsdone
0
130
[SRE NEXT 2025] すみずみまで暖かく照らすあなたの太陽でありたい
carnappopper
2
850
Featured
See All Featured
Become a Pro
speakerdeck
PRO
29
5.4k
Being A Developer After 40
akosma
90
590k
Why Our Code Smells
bkeepers
PRO
337
57k
Responsive Adventures: Dirty Tricks From The Dark Corners of Front-End
smashingmag
251
21k
ReactJS: Keep Simple. Everything can be a component!
pedronauck
667
120k
The Straight Up "How To Draw Better" Workshop
denniskardys
235
140k
個人開発の失敗を避けるイケてる考え方 / tips for indie hackers
panda_program
108
19k
[Rails World 2023 - Day 1 Closing Keynote] - The Magic of Rails
eileencodes
35
2.5k
[RailsConf 2023] Rails as a piece of cake
palkan
55
5.7k
実際に使うSQLの書き方 徹底解説 / pgcon21j-tutorial
soudai
PRO
181
54k
CoffeeScript is Beautiful & I Never Want to Write Plain JavaScript Again
sstephenson
161
15k
Designing for Performance
lara
610
69k
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!