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
81
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
110
Do Androids Dream of Electronic Dance Music?
ericqweinstein
1
93
Machine Learning with Elixir and Phoenix
ericqweinstein
1
930
Machine Learning with Clojure and Apache Spark
ericqweinstein
1
390
A Nil Device, A Lonely Operator, and a Voyage to the Void Star
ericqweinstein
1
950
Other Decks in Technology
See All in Technology
試作とデモンストレーション / Prototyping and Demonstrations
ks91
PRO
0
140
SONiCにて使用されているSAIの実際
sonic
0
140
Azure & DevSecOps
kkamegawa
2
200
計装を見直してアプリケーションパフォーマンスを改善させた話
donkomura
2
170
技術選定の審美眼(2025年版) / Understanding the Spiral of Technologies 2025 edition
twada
PRO
36
10k
WindowsでGenesisに挑戦した話
natsutan
0
110
AIフレンドリーなプロダクト開発を目指して 〜MCPを橋渡しにした環境移行〜
shinpr
0
110
Kaigi Effect 2025 #rubykaigi2025_after
sue445
0
170
Developer 以外にこそ使って欲しい Amazon Q Developer
mita
0
170
dbtとリバースETLでデータ連携の複雑さに立ち向かう
morookacube
0
1k
事業と組織から目を逸らずに技術でリードする
ogugu9
18
4.9k
Cursorを全エンジニアに配布 その先に見据えるAI駆動開発の未来 / 2025-05-13-forkwell-ai-study-1-cursor-at-loglass
itohiro73
2
660
Featured
See All Featured
Building Adaptive Systems
keathley
41
2.5k
What’s in a name? Adding method to the madness
productmarketing
PRO
22
3.4k
The Art of Programming - Codeland 2020
erikaheidi
54
13k
Statistics for Hackers
jakevdp
799
220k
Fight the Zombie Pattern Library - RWD Summit 2016
marcelosomers
233
17k
Exploring the Power of Turbo Streams & Action Cable | RailsConf2023
kevinliebholz
32
5.8k
Improving Core Web Vitals using Speculation Rules API
sergeychernyshev
13
850
Building Flexible Design Systems
yeseniaperezcruz
329
39k
Site-Speed That Sticks
csswizardry
6
550
Docker and Python
trallard
44
3.4k
Balancing Empowerment & Direction
lara
0
31
It's Worth the Effort
3n
184
28k
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!